Journal articles on the topic 'SPECTRAL - SPATIAL STRATEGIES'
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Wefers, Stefanie, Ashish Karmacharya, and Frank Boochs. "Development of a platform recommending 3D and spectral digitisation strategies." Virtual Archaeology Review 7, no. 15 (November 15, 2016): 18. http://dx.doi.org/10.4995/var.2016.5861.
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<p class="VARAbstract" align="left">Spatial and spectral recording of cultural heritage objects is a complex task including data acquisition, processing and analysis involving different technical disciplines. Additionally, the development of a suitable digitisation strategy satisfying the expectations of the humanities experts needs an interdisciplinary dialogue often suffering from misunderstanding and knowledge gaps on both the technical and humanities sides.</p><p class="VARAbstract" align="left">Through a concerted discussion experts from the cultural heritage and technical domains currently develop a so-called COSCH<sup>KR</sup> platform (Colour and Space in Cultural Heritage Knowledge Representation) which will give recommendations for spatial and spectral recording strategies adapted to the needs of the cultural heritage application. The platform will make use of an ontology through which the relevant parameters of the different domains involved in the recording, processing, analysis and dissemination of cultural heritage objects are hierarchically structured and are related through rule-based dependencies. Background and basis for this ontology is the fact that a deterministic relation exists between (1) the requirements of a cultural heritage application on spatial, spectral, as well as visual digital information of a cultural heritage object which itself has concrete physical characteristics and (2) the technical possibilities of the spectral and spatial recording devices. Through a case study which deals with the deformation analysis of wooden samples of cultural heritage artefacts this deterministic relationship is illustrated explaining the overall structure and development of the ontology.</p><p class="VARAbstract" align="left">The aim of the COSCH<sup>KR</sup> platform is to support cultural heritage experts finding the best suitable recording strategy for their often unique physical cultural heritage object and research question. The platform will support them and will make them aware of the relevant parameters and limitations of the recording strategy with respect to the characteristics of the cultural heritage object, external influences, application, recording devices, and data.</p>
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Gircys, Michael, and Brian J. Ross. "Image Evolution Using 2D Power Spectra." Complexity 2019 (January 2, 2019): 1–21. http://dx.doi.org/10.1155/2019/7293193.
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Procedurally generated images and textures have been widely explored in evolutionary art. One active research direction in the field is the discovery of suitable heuristics for measuring perceived characteristics of evolved images. This is important in order to help influence the nature of evolved images and thereby evolve more meaningful and pleasing art. In this regard, particular challenges exist for quantifying aspects of style and shape. In an attempt to bridge the divide between computer vision and cognitive perception, we propose the use of measures related to image spatial frequencies. Based on existing research that uses power spectral density of spatial frequencies as an effective metric for image classification and retrieval, we posit that Fourier decomposition can be effective for guiding image evolution. We refine fitness measures based on Fourier analysis and spatial frequency and apply them within a genetic programming environment for image synthesis. We implement fitness strategies using 2D Fourier power spectra and phase, with the goal of evolving images that share spectral properties of supplied target images. Adaptations and extensions of the fitness strategies are considered for their utility in art systems. Experiments were conducted using a variety of greyscale and colour target images, spatial fitness criteria, and procedural texture languages. Results were promising, in that some target images were trivially evolved, while others were more challenging to characterize. We also observed that some evolved images which we found discordant and “uncomfortable” show a previously identified spectral phenomenon. Future research should further investigate this result, as it could extend the use of 2D power spectra in fitness evaluations to promote new aesthetic properties.
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Maurer, Hansruedi, Stewart Greenhalgh, and Sabine Latzel. "Frequency and spatial sampling strategies for crosshole seismic waveform spectral inversion experiments." GEOPHYSICS 74, no. 6 (November 2009): WCC79—WCC89. http://dx.doi.org/10.1190/1.3157252.
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Analyses of synthetic frequency-domain acoustic waveform data provide new insights into the design and imaging capability of crosshole surveys. The full complex Fourier spectral data offer significantly more information than other data representations such as the amplitude, phase, or Hartley spectrum. Extensive eigenvalue analyses are used for further inspection of the information content offered by the seismic data. The goodness of different experimental configurations is investigated by varying the choice of (1) the frequencies, (2) the source and receiver spacings along the boreholes, and (3) the borehole separation. With only a few carefully chosen frequencies, a similar amount of information can be extracted from the seismic data as can be extracted with a much larger suite of equally spaced frequencies. Optimized data sets should include at least one very low frequencycomponent. The remaining frequencies should be chosen fromthe upper end of the spectrum available. This strategy proved to be applicable to a simple homogeneous and a very complex velocity model. Further tests are required, but it appears on the available evidence to be model independent. Source and receiver spacings also have an effect on the goodness of an experimental setup, but there are only minor benefits to denser sampling when the increment is much smaller than the shortest wavelength included in a data set. If the borehole separation becomes unfavorably large, the information content of the data is degraded, even when many frequencies and small source and receiver spacings are considered. The findings are based on eigenvalue analyses using the true velocity models. Because under realistic conditions the true model is not known, it is shown that the optimized data sets are sufficiently robust to allow the iterative inversion schemes to converge to the global minimum. This is demonstrated by means of tomographic inversions of several optimized data sets.
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Huang, Leping, Zhongwen Hu, Xin Luo, Qian Zhang, Jingzhe Wang, and Guofeng Wu. "Stepwise Fusion of Hyperspectral, Multispectral and Panchromatic Images with Spectral Grouping Strategy: A Comparative Study Using GF5 and GF1 Images." Remote Sensing 14, no. 4 (February 20, 2022): 1021. http://dx.doi.org/10.3390/rs14041021.
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Since hyperspectral satellite images (HSIs) usually hold low spatial resolution, improving the spatial resolution of hyperspectral imaging (HSI) is an effective solution to explore its potential for remote sensing applications, such as land cover mapping over urban and coastal areas. The fusion of HSIs with high spatial resolution multispectral images (MSIs) and panchromatic (PAN) images could be a solution. To address the challenging work of fusing HSIs, MSIs and PAN images, a novel easy-to-implement stepwise fusion approach was proposed in this study. The fusion of HSIs and MSIs was decomposed into a set of simple image fusion tasks through spectral grouping strategy. HSI, MSI and PAN images were fused step by step using existing image fusion algorithms. According to different fusion order, two strategies ((HSI+MSI)+PAN and HSI+(MSI+PAN)) were proposed. Using simulated and real Gaofen-5 (GF-5) HSI, MSI and PAN images from the Gaofen-1 (GF-1) PMS sensor as experimental data, we compared the proposed stepwise fusion strategies with the traditional fusion strategy (HSI+PAN), and compared the performances of six fusion algorithms under three fusion strategies. We comprehensively evaluated the fused results through three aspects: spectral fidelity, spatial fidelity and computation efficiency evaluation. The results showed that (1) the spectral fidelity of the fused images obtained by stepwise fusion strategies was better than that of the traditional strategy; (2) the proposed stepwise strategies performed better or comparable spatial fidelity than traditional strategy; (3) the stepwise strategy did not significantly increase the time complexity compared to the traditional strategy; and (4) we also provide suggestions for selecting image fusion algorithms using the proposed strategy. The study provided us with a reference for the selection of fusion strategies and algorithms in different application scenarios, and also provided an easy-to-implement solution and useful references for fusing HSI, MSI and PAN images.
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Zheng, Cao, Lv, and Benediktsson. "Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images." Remote Sensing 11, no. 16 (August 14, 2019): 1903. http://dx.doi.org/10.3390/rs11161903.
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In this article, a novel approach for land cover change detection (LCCD) using very high resolution (VHR) remote sensing images based on spatial–spectral feature fusion and multi-scale segmentation voting decision is proposed. Unlike other traditional methods that have used a single feature without post-processing on a raw detection map, the proposed approach uses spatial–spectral features and post-processing strategies to improve detecting accuracies and performance. Our proposed approach involved two stages. First, we explored the spatial features of the VHR remote sensing image to complement the insufficiency of the spectral feature, and then fused the spatial–spectral features with different strategies. Next, the Manhattan distance between the corresponding spatial–spectral feature vectors of the bi-temporal images was employed to measure the change magnitude between the bi-temporal images and generate a change magnitude image (CMI). Second, the use of the Otsu binary threshold algorithm was proposed to divide the CMI into a binary change detection map (BCDM) and a multi-scale segmentation voting decision algorithm to fuse the initial BCDMs as the final change detection map was proposed. Experiments were carried out on three pairs of bi-temporal remote sensing images with VHR remote sensing images. The results were compared with those of the state-of-the-art methods including four popular contextual-based LCCD methods and three post-processing LCCD methods. Experimental comparisons demonstrated that the proposed approach had an advantage over other state-of-the-art techniques in terms of detection accuracies and performance.
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Liang, Fan, Cheng Qian, Wei Yu, David Griffith, and Nada Golmie. "Survey of Graph Neural Networks and Applications." Wireless Communications and Mobile Computing 2022 (July 28, 2022): 1–18. http://dx.doi.org/10.1155/2022/9261537.
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The advance of deep learning has shown great potential in applications (speech, image, and video classification). In these applications, deep learning models are trained by datasets in Euclidean space with fixed dimensions and sequences. Nonetheless, the rapidly increasing demands on analyzing datasets in non-Euclidean space require additional research. Generally speaking, finding the relationships of elements in datasets and representing such relationships as weighted graphs consisting of vertices and edges is a viable way of analyzing datasets in non-Euclidean space. However, analyzing the weighted graph-based dataset is a challenging problem in existing deep learning models. To address this issue, graph neural networks (GNNs) leverage spectral and spatial strategies to extend and implement convolution operations in non-Euclidean space. Based on graph theory, a number of enhanced GNNs are proposed to deal with non-Euclidean datasets. In this study, we first review the artificial neural networks and GNNs. We then present ways to extend deep learning models to deal with datasets in non-Euclidean space and introduce the GNN-based approaches based on spectral and spatial strategies. Furthermore, we discuss some typical Internet of Things (IoT) applications that employ spectral and spatial convolution strategies, followed by the limitations of GNNs in the current stage.
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Chandler, Chris J., Silvia Valery Ávila-Mosqueda, Evelyn Raquel Salas-Acosta, Eden Magaña-Gallegos, Edgar Escalante Mancera, Miguel Angel Gómez Reali, Betsabé de la Barreda-Bautista, et al. "Spectral Characteristics of Beached Sargassum in Response to Drying and Decay over Time." Remote Sensing 15, no. 17 (September 2, 2023): 4336. http://dx.doi.org/10.3390/rs15174336.
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The bloom of pelagic Sargassum in the Atlantic Ocean has become increasingly problematic, especially when the algae have beached. A build-up of decaying beached material has damaging effects on coastal ecosystems and tourism industries. While remote sensing offers an effective tool to assess the spatial and temporal patterns of Sargassum over large spatial extents, its use so far has been limited to a broad discrimination of Sargassum species from other macroalgae and floating vegetation. Knowledge on the spatial distribution of decayed material will help to support management strategies and inform targeted removal. In this study, we aim to characterise the spectral response of fresh and decayed Sargassum and identify regions of the spectra that offer the greatest separability for the detection and classification of decayed material. We assessed the spectral response of fresh and decayed Sargassum (1) in situ on the beach and (2) in mesocosm experiments where Sargassum samples were allowed to decay over time. We found a decrease in the magnitude of reflectance, noticeably in the visible region (400–700 nm), for decayed, in contrast to fresh, Sargassum. Separability analyses also showed that most spectral bands with a wavelength > ~540 nm will be capable of discriminating between fresh and decayed material, although the near-infrared region offers the greatest degree of separability. We demonstrate, for the first time, that there are clear differences in the spectral reflectance of fresh and decayed Sargassum with potential application for remote sensing approaches.
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Gojani, Ardian B., Dávid J. Palásti, Andrea Paul, Gábor Galbács, and Igor B. Gornushkin. "Analysis and Classification of Liquid Samples Using Spatial Heterodyne Raman Spectroscopy." Applied Spectroscopy 73, no. 12 (August 1, 2019): 1409–19. http://dx.doi.org/10.1177/0003702819863847.
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Spatial heterodyne spectroscopy (SHS) is used for quantitative analysis and classification of liquid samples. SHS is a version of a Michelson interferometer with no moving parts and with diffraction gratings in place of mirrors. The instrument converts frequency-resolved information into a spatially resolved one and records it in the form of interferograms. The back-extraction of spectral information is done by the fast Fourier transform. A SHS instrument is constructed with the resolving power 5000 and spectral range 522–593 nm. Two original technical solutions are used as compared to previous SHS instruments: the use of a high-frequency diode-pumped solid-state laser for excitation of Raman spectra and a microscope-based collection system. Raman spectra are excited at 532 nm at the repetition rate 80 kHz. Raman shifts between 330 cm−1 and 1600 cm−1 are measured. A new application of SHS is demonstrated: for the first time, it is used for quantitative Raman analysis to determine concentrations of cyclohexane in isopropanol and glycerol in water. Two calibration strategies are employed: univariate based on the construction of a calibration plot and multivariate based on partial least squares regression. The detection limits for both cyclohexane in isopropanol and glycerol in water are at a 0.5 mass% level. In addition to the Raman–SHS chemical analysis, classification of industrial oils (biodiesel, poly(1-decene), gasoline, heavy oil IFO380, polybutenes, and lubricant) is performed using the Raman–fluorescence spectra of the oils and principal component analysis. The oils are easily discriminated showing distinct non-overlapping patterns in the principal component space.
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Sun, Jun, Junbo Zhang, Xuesong Gao, Mantao Wang, Dinghua Ou, Xiaobo Wu, and Dejun Zhang. "Fusing Spatial Attention with Spectral-Channel Attention Mechanism for Hyperspectral Image Classification via Encoder–Decoder Networks." Remote Sensing 14, no. 9 (April 19, 2022): 1968. http://dx.doi.org/10.3390/rs14091968.
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In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. However, feature extraction on hyperspectral data still faces numerous challenges. Existing methods cannot extract spatial and spectral-channel contextual information in a targeted manner. In this paper, we propose an encoder–decoder network that fuses spatial attention and spectral-channel attention for HSI classification from three public HSI datasets to tackle these issues. In terms of feature information fusion, a multi-source attention mechanism including spatial and spectral-channel attention is proposed to encode the spatial and spectral multi-channels contextual information. Moreover, three fusion strategies are proposed to effectively utilize spatial and spectral-channel attention. They are direct aggregation, aggregation on feature space, and Hadamard product. In terms of network development, an encoder–decoder framework is employed for hyperspectral image classification. The encoder is a hierarchical transformer pipeline that can extract long-range context information. Both shallow local features and rich global semantic information are encoded through hierarchical feature expressions. The decoder consists of suitable upsampling, skip connection, and convolution blocks, which fuse multi-scale features efficiently. Compared with other state-of-the-art methods, our approach has greater performance in hyperspectral image classification.
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Miao, Zelang, and Wenzhong Shi. "A New Methodology for Spectral-Spatial Classification of Hyperspectral Images." Journal of Sensors 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/1538973.
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Recent developments in hyperspectral images have heightened the need for advanced classification methods. To reach this goal, this paper proposed an improved spectral-spatial method for hyperspectral image classification. The proposed method mainly consists of three steps. First, four band selection strategies are proposed to utilize the statistical region merging (SRM) method to segment the hyperspectral image. The segmentation map is subsequently integrated with the pixel-wise classification method to classify the hyperspectral image. Finally, the final classification result is obtained using the decision fusion rule. Validation tests are performed to evaluate the performance of the proposed approach, and the results indicate that the new proposed approach outperforms the state-of-the-art methods.
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Rud, Ronit, Maxim Shoshany, and Victor Alchanatis. "Spatial–spectral processing strategies for detection of salinity effects in cauliflower, aubergine and kohlrabi." Biosystems Engineering 114, no. 4 (April 2013): 384–96. http://dx.doi.org/10.1016/j.biosystemseng.2012.11.012.
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Zhu, Hongyan, Aoife Gowen, Hailin Feng, Keping Yu, and Jun-Li Xu. "Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products." Sensors 20, no. 18 (September 17, 2020): 5322. http://dx.doi.org/10.3390/s20185322.
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Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe (“myocommata”) and red muscle (“myotome”) pixels on salmon fillets. Results showed that combining spectral-spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre-processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.
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Heiden, Uta, Pablo d’Angelo, Peter Schwind, Paul Karlshöfer, Rupert Müller, Simone Zepp, Martin Wiesmeier, and Peter Reinartz. "Soil Reflectance Composites—Improved Thresholding and Performance Evaluation." Remote Sensing 14, no. 18 (September 10, 2022): 4526. http://dx.doi.org/10.3390/rs14184526.
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Reflectance composites that capture bare soil pixels from multispectral image data are increasingly being analysed to model soil constituents such as soil organic carbon. These temporal composites are used instead of single-date multispectral images to account for the frequent vegetation cover of soils and, thus, to get broader spatial coverage of bare soil pixels. Most soil compositing techniques require thresholds derived from spectral indices such as the Normalised Difference Vegetation Index (NDVI) and the Normalised Burn Ratio 2 (NBR2) to separate bare soils from all other land cover types. However, the threshold derivation is handled based on expert knowledge of a specific area, statistical percentile definitions or in situ data. For operational processors, such site-specific and partly manual strategies are not applicable. There is a need for a more generic solution to derive thresholds for large-scale processing without manual intervention. This study presents a novel HIstogram SEparation Threshold (HISET) methodology deriving spectral index thresholds and testing them for a Sentinel-2 temporal data stack. The technique is spectral index-independent, data-driven and can be evaluated based on a quality score. We tested HISET for building six soil reflectance composites (SRC) using NDVI, NBR2 and a new index combining the NDVI and a short-wave infrared (SWIR) band (PV+IR2). A comprehensive analysis of the spectral and spatial performance and accuracy of the resulting SRCs proves the flexibility and validity of HISET. Disturbance effects such as spectral confusion of bare soils with non-photosynthetic-active vegetation (NPV) could be reduced by choosing grassland and crops as input LC for HISET. The NBR2-based SRC spectra showed the highest similarity with LUCAS spectra, the broadest spatial coverage of bare soil pixels and the least number of valid observations per pixel. The spatial coverage of bare soil pixels is validated against the database of the Integrated Administration and Control System (IACS) of the European Commission. Validation results show that PV+IR2-based SRCs outperform the other two indices, especially in spectrally mixed areas of bare soil, photosynthetic-active vegetation and NPV. The NDVI-based SRCs showed the lowest confidence values (95%) in all bands. In the future, HISET shall be tested in other areas with different environmental conditions and LC characteristics to evaluate if the findings of this study are also valid.
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Liu, Zhaohua, Jiangping Long, Hui Lin, Kai Du, Xiaodong Xu, Hao Liu, Peisong Yang, Tingchen Zhang, and Zilin Ye. "Interpretation and Mapping Tree Crown Diameter Using Spatial Heterogeneity in Relation to the Radiative Transfer Model Extracted from GF-2 Images in Planted Boreal Forest Ecosystems." Remote Sensing 15, no. 7 (March 28, 2023): 1806. http://dx.doi.org/10.3390/rs15071806.
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Tree crown diameter (CD) values, relating to the rate of material exchange between the forest and the atmosphere, can be used to evaluate forest biomass and carbon stock. To map tree CD values using meter-level optical remote sensing images, we propose a novel method that interprets the relationships between the spectral reflectance of pixels and the CD. The approach employs the spectral reflectance of pixels in the tree crown to express the diversity of inclination angles of leaves based on the radiative transfer model and the spatial heterogeneity of these pixels. Then, simulated and acquired GF-2 images are applied to verify the relationships between spatial heterogeneity and the tree CD. Meanwhile, filter-based and object-based methods are also employed to extract three types of variables (spectral features, texture features, and spatial heterogeneity). Finally, the tree CD values are mapped by four models (random forest (RF), K-nearest neighbor (K-NN), support vector machine (SVM), and multiple linear regression (MLR)), using three single types of variables and combinations of variables with different strategies. The results imply that the spatial heterogeneity of spectral reflectance is significantly positively correlated with tree CD values and is more sensitive to tree CD values than traditional spectral features and textural features. Furthermore, the ability of spatial heterogeneity to map tree CD values is significantly higher than traditional variable sets after obtaining stable features with appropriate filter window sizes. The results also demonstrate that the accuracy of mapped tree CD values is significantly improved using combined variable sets with different feature extraction methods. For example, in our experiments, the R2 and rRMSE values of the optimal results ranged from 0.60 to 0.66, and from 15.76% to 16.68%, respectively. It is confirmed that spatial heterogeneity with high sensitivity can effectively map tree CD values, and the accuracy of mapping tree CD values can be greatly improved using a combination of spectral features extracted by an object-based method and spatial heterogeneity extracted by a filter-based method.
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Li, Na, Chengeng Gong, Huijie Zhao, and Yun Ma. "Space Target Material Identification Based on Graph Convolutional Neural Network." Remote Sensing 15, no. 7 (April 4, 2023): 1937. http://dx.doi.org/10.3390/rs15071937.
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Under complex illumination conditions, the spectral data distributions of a given material appear inconsistent in the hyperspectral images of the space target, making it difficult to achieve accurate material identification using only spectral features and local spatial features. Aiming at this problem, a material identification method based on an improved graph convolutional neural network is proposed. Superpixel segmentation is conducted on the hyperspectral images to build the multiscale joint topological graph of the space target global structure. Based on this, topological graphs containing the global spatial features and spectral features of each pixel are generated, and the pixel neighborhoods containing the local spatial features and spectral features are collected to form material identification datasets that include both of these. Then, the graph convolutional neural network (GCN) and the three-dimensional convolutional neural network (3-D CNN) are combined into one model using strategies of addition, element-wise multiplication, or concatenation, and the model is trained by the datasets to fuse and learn the three features. For the simulated data and the measured data, the overall accuracy of the proposed method can be kept at 85–90%, and their kappa coefficients remain around 0.8. This proves that the proposed method can improve the material identification performance under complex illumination conditions with high accuracy and strong robustness.
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Sun, Hezhi, Ke Zheng, Ming Liu, Chao Li, Dong Yang, and Jindong Li. "Hyperspectral Image Mixed Noise Removal Using a Subspace Projection Attention and Residual Channel Attention Network." Remote Sensing 14, no. 9 (April 26, 2022): 2071. http://dx.doi.org/10.3390/rs14092071.
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Although the existing deep-learning-based hyperspectral image (HSI) denoising methods have achieved tremendous success, recovering high-quality HSIs in complex scenes that contain mixed noise is still challenging. Besides, these methods have not fully explored the local and global spatial–spectral information of HSIs. To address the above issues, a novel HSI mixed noise removal network called subspace projection attention and residual channel attention network (SPARCA-Net) is proposed. Specifically, we propose an orthogonal subspace projection attention (OSPA) module to adaptively learn to generate bases of the signal subspace and project the input into such space to remove noise. By leveraging the local and global spatial relations, OSPA is able to reconstruct the local structure of the feature maps more precisely. We further propose a residual channel attention (RCA) module to emphasize the interdependence between feature maps and exploit the global channel correlation of them, which could enhance the channel-wise adaptive learning. In addition, multiscale joint spatial–spectral input and residual learning strategies are employed to capture multiscale spatial–spectral features and reduce the degradation problem, respectively. Synthetic and real HSI data experiments demonstrated that the proposed HSI denoising network outperforms many of the advanced methods in both quantitative and qualitative assessments.
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Müllerová, Jana, Josef Brůna, Petr Dvořák, Tomáš Bartaloš, and Michaela Vítková. "DOES THE DATA RESOLUTION/ORIGIN MATTER? SATELLITE, AIRBORNE AND UAV IMAGERY TO TACKLE PLANT INVASIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 903–8. http://dx.doi.org/10.5194/isprs-archives-xli-b7-903-2016.
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Invasive plant species represent a serious threat to biodiversity and landscape as well as human health and socio-economy. To successfully fight plant invasions, new methods enabling fast and efficient monitoring, such as remote sensing, are needed. In an ongoing project, optical remote sensing (RS) data of different origin (satellite, aerial and UAV), spectral (panchromatic, multispectral and color), spatial (very high to medium) and temporal resolution, and various technical approaches (object-, pixelbased and combined) are tested to choose the best strategies for monitoring of four invasive plant species (giant hogweed, black locust, tree of heaven and exotic knotweeds). In our study, we address trade-offs between spectral, spatial and temporal resolutions required for balance between the precision of detection and economic feasibility. For the best results, it is necessary to choose best combination of spatial and spectral resolution and phenological stage of the plant in focus. For species forming distinct inflorescences such as giant hogweed iterative semi-automated object-oriented approach was successfully applied even for low spectral resolution data (if pixel size was sufficient) whereas for lower spatial resolution satellite imagery or less distinct species with complicated architecture such as knotweed, combination of pixel and object based approaches was used. High accuracies achieved for very high resolution data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparably precise, fast and efficient. This knowledge serves as a basis for prediction, monitoring and prioritization of management targets.
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Müllerová, Jana, Josef Brůna, Petr Dvořák, Tomáš Bartaloš, and Michaela Vítková. "DOES THE DATA RESOLUTION/ORIGIN MATTER? SATELLITE, AIRBORNE AND UAV IMAGERY TO TACKLE PLANT INVASIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 903–8. http://dx.doi.org/10.5194/isprsarchives-xli-b7-903-2016.
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Invasive plant species represent a serious threat to biodiversity and landscape as well as human health and socio-economy. To successfully fight plant invasions, new methods enabling fast and efficient monitoring, such as remote sensing, are needed. In an ongoing project, optical remote sensing (RS) data of different origin (satellite, aerial and UAV), spectral (panchromatic, multispectral and color), spatial (very high to medium) and temporal resolution, and various technical approaches (object-, pixelbased and combined) are tested to choose the best strategies for monitoring of four invasive plant species (giant hogweed, black locust, tree of heaven and exotic knotweeds). In our study, we address trade-offs between spectral, spatial and temporal resolutions required for balance between the precision of detection and economic feasibility. For the best results, it is necessary to choose best combination of spatial and spectral resolution and phenological stage of the plant in focus. For species forming distinct inflorescences such as giant hogweed iterative semi-automated object-oriented approach was successfully applied even for low spectral resolution data (if pixel size was sufficient) whereas for lower spatial resolution satellite imagery or less distinct species with complicated architecture such as knotweed, combination of pixel and object based approaches was used. High accuracies achieved for very high resolution data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparably precise, fast and efficient. This knowledge serves as a basis for prediction, monitoring and prioritization of management targets.
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Blais, J. "Discrete Spherical Harmonic Transforms for Equiangular Grids of Spatial and Spectral Data." Journal of Geodetic Science 1, no. 1 (March 1, 2011): 9–16. http://dx.doi.org/10.2478/v10156-010-0002-7.
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Discrete Spherical Harmonic Transforms for Equiangular Grids of Spatial and Spectral DataSpherical Harmonic Transforms (SHTs) which are non-commutative Fourier transforms on the sphere are critical in global geopotential and related applications. Among the best known global strategies for discrete SHTs of band-limited spherical functions are Chebychev quadratures and least squares for equiangular grids. With proper numerical preconditioning, independent of latitude, reliable analysis and synthesis results for degrees and orders over 3800 in double precision arithmetic have been achieved and explicitly demonstrated using white noise simulations. The SHT synthesis and analysis can easily be modified for the ordinary Fourier transform of the data matrix and the mathematical situation is illustrated in a new functional diagram. Numerical analysis has shown very little differences in the numerical conditioning and computational efforts required when working with the two-dimensional (2D) Fourier transform of the data matrix. This can be interpreted as the spectral form of the discrete SHT which can be useful in multiresolution and other applications. Numerical results corresponding to the latest Earth Geopotential Model EGM 2008 of maximum degree and order 2190 are included with some discussion of the implications when working with such spectral sequences of fast decreasing magnitude.
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Liu, Zhaohua, Jiangping Long, Hui Lin, Xiaodong Xu, Hao Liu, Tingchen Zhang, Zilin Ye, and Peisong Yang. "Combination Strategies of Variables with Various Spatial Resolutions Derived from GF-2 Images for Mapping Forest Stock Volume." Forests 14, no. 6 (June 6, 2023): 1175. http://dx.doi.org/10.3390/f14061175.
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Spectral features (SFs) and texture features (TFs) extracted from optical remote sensing images can capture the structural composition and growth information of forests, and combining remote sensing variables with a few ground measurement samples is a common method for mapping forest stock volume (FSV). However, the accuracy of mapping FSV using optical images with a high spatial resolution (one meter or sub-meters) is often lower than medium resolutions (larger than 10 m) using the same types of features and approaches. To overcome the limitations of high spatial resolution images in mapping FSV, down-scaled images with spatial resolution ranging from 1 to 30 m were obtained by GF-2 image to interpret the relationships between spatial resolutions of features and the accuracy of mapping FSV, and combination strategies of variables with various spatial resolutions were proposed to improve the accuracy of mapping FSV. The results show that the spatial resolution of features significantly affects the performance of employed models in estimating FSV, the sensitivity between SFs and FSV gradually increases with the decreasing of spatial resolution, and the optimal spatial resolutions of two types of features (SFs and TFs) are not synchronized in mapping forest FSV. After using combination strategies of variables with various spatial resolutions, the accuracy of mapping FSV is significantly higher than those derived from variable sets with the same spatial resolutions. It is proved that TFs derived from GF-2 images have great potential to improve the accuracy of mapping FSV, and the contribution of features depends on the approaches of extracting and combination strategies.
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Asyraf, Muhammad A., and Dhany Arifianto. "Effect of electric-acoustic cochlear implant stimulation and coding strategies on spatial cues of speech signals in reverberant room." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A195. http://dx.doi.org/10.1121/10.0016005.
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The comparison of spatial cues changes in different setups and coding strategies used in cochlear implants (CI) is investigated. In this experiment, we implement three voice coder setups, such as bilateral CI, bimodal CI, and electro-acoustic stimulation (EAS). Two well-known coding strategies are used, which are continuous interleaved sampling (CIS) and spectral peak (SPEAK). Speech signals are convoluted with appropriate binaural room impulse response (BRIR), creating reverberant spatial stimuli. Five different reverberant conditions (including anechoic) were applied to the stimuli. Interaural level and time differences (ILD and ITD) are evaluated objectively and subjectively, and their relationship with the intelligibility of speech is observed. Prior objective evaluation with CIS reveals that clarity (C50) becomes a more important factor in spatial cue change than reverberation time. Vocoded conditions (bilateral CI) show an increment in ILD value (compression has not been implemented yet on the vocoder processing), when the value of ITD gets more different (decreased) from the middle point. Reverberation degrades the intelligibility rate at various rates depending on the C50 value, both in unvocoded and vocoded conditions. In the vocoded condition, decrement on spatial cues was also followed by the decreement on the intelligibility of spatial stimuli.
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Ndou, Naledzani, Kgabo Humphrey Thamaga, Yonela Mndela, and Adolph Nyamugama. "Radiometric Compensation for Occluded Crops Imaged Using High-Spatial-Resolution Unmanned Aerial Vehicle System." Agriculture 13, no. 8 (August 12, 2023): 1598. http://dx.doi.org/10.3390/agriculture13081598.
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Crop characterization is considered a prerequisite to devising effective strategies for ensuring successful implementation of sustainable agricultural management strategies. As such, remote-sensing technology has opened an exciting horizon for crop characterization at reasonable spatial, spectral, and temporal scales. However, the presence of shadows on croplands tends to distort radiometric properties of the crops, subsequently limiting the retrieval of crop-related information. This study proposes a simple and reliable approach for radiometrically compensating crops under total occlusion using brightness-based compensation and thresholding approaches. Unmanned aerial vehicle (UAV) imagery was used to characterize crops at the experimental site. In this study, shadow was demarcated through the computation and use of mean spectral radiance values as the threshold across spectral channels of UAV imagery. Several image classifiers, viz., k-nearest neighbor (KNN), maximum likelihood, multilayer perceptron (MLP), and image segmentation, were used to categorize land features, with a view to determine the areal coverage of crops prior to the radiometric compensation process. Radiometric compensation was then performed to restore radiometric properties of land features under occlusion by performing brightness tuning on the RGB imagery. Radiometric compensation results revealed maize and soil as land features subjected to occlusion. The relative error of the mean results for radiance comparison between lit and occluded regions revealed 26.47% deviation of the restored radiance of occluded maize from that of lit maize. On the other hand, the reasonable REM value of soil was noted to be 50.92%, implying poor radiometric compensation results. Postradiometric compensation classification results revealed increases in the areal coverage of maize cultivars and soil by 40.56% and 12.37%, respectively, after being radiometrically compensated, as predicted by the KNN classifier. The maximum likelihood, MLP, and segmentation classifiers predicted increases in area covered with maize of 18.03%, 22.42%, and 30.64%, respectively. Moreover, these classifiers also predicted increases in the area covered with soil of 1.46%, 10.05%, and 14.29%, respectively. The results of this study highlight the significance of brightness tuning and thresholding approaches in radiometrically compensating occluded crops.
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Bley-Dalouman, H., F. Broust, J. Prevost, and A. Tran. "USE OF VERY HIGH SPATIAL RESOLUTION IMAGERY FOR MAPPING WOOD ENERGY POTENTIAL FROM TROPICAL MANAGED FOREST STANDS, REUNION ISLAND." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 189–94. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-189-2021.
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Abstract. The development of a sustainable wood energy chain is an essential part of ecological and energy transition in Reunion Island (Indian Ocean), where Acacia mearnsii is the main potential wood energy resource identified to date. In order to assess future wood biomass supply chain strategies, a major first issue is to gain knowledge of the spatial distribution of this species forest stands.In this study, we assessed the potential of very high spatial resolution multispectral imagery for mapping the main forest stands in a study area located the Western Highlands region, where Acacia mearnsii expands alongside Acacia heterophylla, an endemic forest species and Cryptomeria japonica, an exotic forest stand. A reference database including 150 samples of seven classes (Acacia mearnsii (mature and non-mature), Acacia heterophylla (mature and non-mature), Cryptomeria japonica, ‘herbaceous areas’, and ‘bare soils’) was used to classify a Pleiades image acquired in May 2020. Spectral and textural indices were used in an incremental classification procedure using a random classifier.The best results (Kappa = 0.84, global accuracy = 84%) were obtained for the classification using all spectral and textural bands. The resulting map enables analyzing the spatial distribution of the different forest stands.
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Arun, Pattathal Vijayakumar. "SPATIAL ANALYSIS IN PUBLIC HEALTH DOMAIN: AN NLP APPROACH." Geodesy and Cartography 39, no. 4 (December 18, 2013): 149–57. http://dx.doi.org/10.3846/20296991.2013.871140.
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Remote sensing products are effectively used as a tool for decision making in various fields, especially in medical research and health care analyses. GIS is particularly well suited in this context because of its spatial analysis and display capabilities. The integration of RS techniques in public health has been categorised as continuous and discrete strategies where latter is preferred. We have investigated the integration of these approaches through linguistic interpretation of images. In this paper, we propose a framework for direct natural language interpretation of satellite images using probabilistic grammar rules in conjunction with evolutionary computing techniques. Spectral and spatial information has been dynamically combined using adaptive kernel strategy for effective representation of the contextual knowledge. The developed methodology has been evaluated in different querying contexts and investigations revealed that considerable success has been achieved with the procedure. The methodology has also demonstrated to be effective in intelligent interpolation, automatic interpretation as well as attribute, topology, proximity, and semantic analyses.
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Knauer, Uwe, Cornelius Styp von Rekowski, Marianne Stecklina, Tilman Krokotsch, Tuan Pham Minh, Viola Hauffe, David Kilias, et al. "Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers." Remote Sensing 11, no. 23 (November 26, 2019): 2788. http://dx.doi.org/10.3390/rs11232788.
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In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).
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Martello, Maurício, José Paulo Molin, Marcelo Chan Fu Wei, Ricardo Canal Canal Filho, and João Vitor Moreira Nicoletti. "Coffee-Yield Estimation Using High-Resolution Time-Series Satellite Images and Machine Learning." AgriEngineering 4, no. 4 (October 5, 2022): 888–902. http://dx.doi.org/10.3390/agriengineering4040057.
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Coffee has high relevance in the Brazilian agricultural scenario, as Brazil is the largest producer and exporter of coffee in the world. Strategies to advance the production of coffee grains involve better understanding its spatial variability along fields. The objectives of this study were to adjust yield-prediction models based on a time series of satellite images and high-density yield data, and to indicate the best phenological stage of coffee crop to obtain satellite images for this purpose. The study was conducted during three seasons (2019, 2020 and 2021) in a commercial area (10.24 ha), located in the state of Minas Gerais, Brazil. Data were obtained using a harvester equipped with a yield monitor that measures the volume of coffee harvested with 3.0 m of spatial resolution. Satellite images from the PlanetScope (PS) platform were used. Random forest (RF) regression and multiple linear regression (MLR) models were fitted to different datasets composed of coffee yield and time series of satellite-image data ((1) Spectral bands—red, green, blue and near-infrared; (2) Normalized difference vegetation index (NDVI); or (3) Green normalized difference vegetation index (GNDVI)). Whether using RF or MLR, the spectral bands, NDVI and GNDVI reproduced the spatial variability of yield maps one year before harvest. This information can be of critical importance for management decisions across the season. For yield quantification, the RF model using spectral bands showed the best results, reaching R² of 0.93 for the validation set, and the lowest errors of prediction. The most appropriate phenological stage for satellite-image data acquisition was the dormancy phase, observed during the dry season months of July and August. These findings can help to monitor the spatial and temporal variability of the fields and guide management practices based on the premises of precision agriculture.
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Mehdi Haghshenas, Francesco Linsalata, Luca Barbieri, Mattia Brambilla, Monica Nicoli, and Maurizio Magarini. "Analysis of spatial scheduling in downlink vehicular communications: Sub-6 GHz vs mmWave." ITU Journal on Future and Evolving Technologies 3, no. 2 (September 30, 2022): 523–34. http://dx.doi.org/10.52953/gewx7355.
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Vehicular communications are gaining a lot of attention for the delivery of enhanced mobility services that require multi-Gbps and low latency connections. In this paper, we focus on Infrastructure-to-Vehicle (I2V) communications where a gNB has to assign spatial resources to a number of connected vehicle users. To efficiently manage the scheduling, we compare the Zero Forcing (ZF) and Maximum Ratio (MR) precoding strategies by evaluating the effect of shifting from sub-6 GHz to millimeter wave (mmWave) frequencies in urban and highway mobility scenarios. We analyze the impact of the geometry of the environment and propagation characteristics at different frequencies in terms of number of users that can be served and spectral efficiency. To model the I2V channel, we integrate realistic traffic conditions generated by SUMO into an accurate channel model based on ray tracing software by WirelessInsite. By numerical results we demonstrate the degradation at mmWave compared to sub-6 GHz on the multiplexing gain. We show the higher efficiency of ZF compared to MR as the former is not limited by inter-user interference, especially in urban scenarios where the number of distinctive eigendirections in space is limited. On the other hand, highway mobility has a more uniform distribution of vehicles that can be conveniently explored by the ZF scheduling to serve more users. Lastly, we show the benefits of adopting a higher number of transmit antennas at mmWave jointly with efficient scheduling to achieve higher spectral efficiency.
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Gautam, Deepak, Bertram Ostendorf, and Vinay Pagay. "Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle." Remote Sensing 13, no. 13 (July 5, 2021): 2639. http://dx.doi.org/10.3390/rs13132639.
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Crop water status and irrigation requirements are of great importance to the horticultural industry due to changing climatic conditions leading to high evaporative demands, drought and water scarcity in semi-arid and arid regions worldwide. Irrigation scheduling strategies based on evapotranspiration (ET), such as regulated deficit irrigation, requires the estimation of seasonal crop coefficients (kc). The ET-driven irrigation decisions for grapevines rely on the sampling of several kc values from each irrigation zone. Here, we present an unmanned aerial vehicle (UAV)-based technique to estimate kc at the single vine level in order to capture the spatial variability of water requirements in a commercial vineyard located in South Australia. A UAV carrying a multispectral sensor is used to extract the spectral, as well as the structural, information of Cabernet Sauvignon grapevines. The spectral and structural information, acquired at the various phenological stages of the vine through two seasons, is used to model kc using univariate (simple linear), multivariate (generalised linear and additive) and machine learning (convolution neural network and random forest) model frameworks. The structural information (e.g., canopy top view area) had the strongest correlation with kc throughout the season (p ≤ 0.001; Pearson R = 0.56), while the spectral indices (e.g., normalised indices) turned less-sensitive post véraison—the onset of ripening in grapes. Combining structural and spectral information improved the model’s performance. Among the investigated predictive models, the random forest predicted kc with the highest accuracy (R2: 0.675, root mean square error: 0.062, and mean absolute error: 0.047). This UAV-based approach improves the precision of irrigation by capturing the spatial variability of kc within a vineyard. Combined with an energy balance model, the water needs of a vineyard can be computed on a weekly or sub-weekly basis for precision irrigation. The UAV-based characterisation of kc can further enhance the water management and irrigation zoning by matching the infrastructure with the spatial variability of the irrigation demand.
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Yi, Zhiwei, Li Jia, and Qiting Chen. "Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China." Remote Sensing 12, no. 24 (December 11, 2020): 4052. http://dx.doi.org/10.3390/rs12244052.
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Timely and accurate crop classification is of enormous significance for agriculture management. The Shiyang River Basin, an inland river basin, is one of the most prominent water resource shortage regions with intensive agriculture activities in northwestern China. However, a free crop map with high spatial resolution is not available in the Shiyang River Basin. The European Space Agency (ESA) satellite Sentinel-2 has multi-spectral bands ranging in the visible-red edge-near infrared-shortwave infrared (VIS-RE-NIR-SWIR) spectrum. Understanding the impact of spectral-temporal information on crop classification is helpful for users to select optimized spectral bands combinations and temporal window in crop mapping when using Sentinel-2 data. In this study, multi-temporal Sentinel-2 data acquired in the growing season in 2019 were applied to the random forest algorithm to generate the crop classification map at 10 m spatial resolution for the Shiyang River Basin. Four experiments with different combinations of feature sets were carried out to explore which Sentinel-2 information was more effective for higher crop classification accuracy. The results showed that the augment of multi-spectral and multi-temporal information of Sentinel-2 improved the accuracy of crop classification remarkably, and the improvement was firmly related to strategies of feature selections. Compared with other bands, red-edge band 1 (RE-1) and shortwave-infrared band 1 (SWIR-1) of Sentinel-2 showed a higher competence in crop classification. The combined application of images in the early, middle and late crop growth stage is significant for achieving optimal performance. A relatively accurate classification (overall accuracy = 0.94) was obtained by utilizing the pivotal spectral bands and dates of image. In addition, a crop map with a satisfied accuracy (overall accuracy > 0.9) could be generated as early as late July. This study gave an inspiration in selecting targeted spectral bands and period of images for acquiring more accurate and timelier crop map. The proposed method could be transferred to other arid areas with similar agriculture structure and crop phenology.
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Pascual-Venteo, Ana B., Enrique Portalés, Katja Berger, Giulia Tagliabue, Jose L. Garcia, Adrián Pérez-Suay, Juan Pablo Rivera-Caicedo, and Jochem Verrelst. "Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data." Remote Sensing 14, no. 10 (May 19, 2022): 2448. http://dx.doi.org/10.3390/rs14102448.
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In preparation for new-generation imaging spectrometer missions and the accompanying unprecedented inflow of hyperspectral data, optimized models are needed to generate vegetation traits routinely. Hybrid models, combining radiative transfer models with machine learning algorithms, are preferred, however, dealing with spectral collinearity imposes an additional challenge. In this study, we analyzed two spectral dimensionality reduction methods: principal component analysis (PCA) and band ranking (BR), embedded in a hybrid workflow for the retrieval of specific leaf area (SLA), leaf area index (LAI), canopy water content (CWC), canopy chlorophyll content (CCC), the fraction of absorbed photosynthetic active radiation (FAPAR), and fractional vegetation cover (FVC). The SCOPE model was used to simulate training data sets, which were optimized with active learning. Gaussian process regression (GPR) algorithms were trained over the simulations to obtain trait-specific models. The inclusion of PCA and BR with 20 features led to the so-called GPR-20PCA and GPR-20BR models. The 20PCA models encompassed over 99.95% cumulative variance of the full spectral data, while the GPR-20BR models were based on the 20 most sensitive bands. Validation against in situ data obtained moderate to optimal results with normalized root mean squared error (NRMSE) from 13.9% (CWC) to 22.3% (CCC) for GPR-20PCA models, and NRMSE from 19.6% (CWC) to 29.1% (SLA) for GPR-20BR models. Overall, the GPR-20PCA slightly outperformed the GPR-20BR models for all six variables. To demonstrate mapping capabilities, both models were tested on a PRecursore IperSpettrale della Missione Applicativa (PRISMA) scene, spectrally resampled to Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), over an agricultural test site (Jolanda di Savoia, Italy). The two strategies obtained plausible spatial patterns, and consistency between the two models was highest for FVC and LAI (R2=0.91, R2=0.86) and lowest for SLA mapping (R2=0.53). From these findings, we recommend implementing GPR-20PCA models as the most efficient strategy for the retrieval of multiple crop traits from hyperspectral data streams. Hence, this workflow will support and facilitate the preparations of traits retrieval models from the next-generation operational CHIME.
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Zhu, Chuanmei, Zipeng Zhang, Hongwei Wang, Jingzhe Wang, and Shengtian Yang. "Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions." Sensors 20, no. 6 (March 24, 2020): 1795. http://dx.doi.org/10.3390/s20061795.
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Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg−1) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R2), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map.
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Kakhani, Nafiseh, Mehdi Mokhtarzade, and Mohammad Javad Valadan Zoej. "Deep Learning Spatial-Spectral Classification of Remote Sensing Images by Applying Morphology-Based Differential Extinction Profile (DEP)." Electronics 10, no. 23 (November 23, 2021): 2893. http://dx.doi.org/10.3390/electronics10232893.
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Since the technology of remote sensing has been improved recently, the spatial resolution of satellite images is getting finer. This enables us to precisely analyze the small complex objects in a scene through remote sensing images. Thus, the need to develop new, efficient algorithms like spatial-spectral classification methods is growing. One of the most successful approaches is based on extinction profile (EP), which can extract contextual information from remote sensing data. Moreover, deep learning classifiers have drawn attention in the remote sensing community in the past few years. Recent progress has shown the effectiveness of deep learning at solving different problems, particularly segmentation tasks. This paper proposes a novel approach based on a new concept, which is differential extinction profile (DEP). DEP makes it possible to have an input feature vector with both spectral and spatial information. The input vector is then fed into a proposed straightforward deep-learning-based classifier to produce a thematic map. The approach is carried out on two different urban datasets from Pleiades and World-View 2 satellites. In order to prove the capabilities of the suggested approach, we compare the final results to the results of other classification strategies with different input vectors and various types of common classifiers, such as support vector machine (SVM) and random forests (RF). It can be concluded that the proposed approach is significantly improved in terms of three kinds of criteria, which are overall accuracy, Kappa coefficient, and total disagreement.
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Tahmasebi, Sina, Manuel Segovia-Martinez, and Waldo Nogueira. "Optimization of Sound Coding Strategies to Make Singing Music More Accessible for Cochlear Implant Users." Trends in Hearing 27 (January 2023): 233121652211480. http://dx.doi.org/10.1177/23312165221148022.
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Cochlear implants (CIs) are implantable medical devices that can partially restore hearing to people suffering from profound sensorineural hearing loss. While these devices provide good speech understanding in quiet, many CI users face difficulties when listening to music. Reasons include poor spatial specificity of electric stimulation, limited transmission of spectral and temporal fine structure of acoustic signals, and restrictions in the dynamic range that can be conveyed via electric stimulation of the auditory nerve. The coding strategies currently used in CIs are typically designed for speech rather than music. This work investigates the optimization of CI coding strategies to make singing music more accessible to CI users. The aim is to reduce the spectral complexity of music by selecting fewer bands for stimulation, attenuating the background instruments by strengthening a noise reduction algorithm, and optimizing the electric dynamic range through a back-end compressor. The optimizations were evaluated through both objective and perceptual measures of speech understanding and melody identification of singing voice with and without background instruments, as well as music appreciation questionnaires. Consistent with the objective measures, results gathered from the perceptual evaluations indicated that reducing the number of selected bands and optimizing the electric dynamic range significantly improved speech understanding in music. Moreover, results obtained from questionnaires show that the new music back-end compressor significantly improved music enjoyment. These results have potential as a new CI program for improved singing music perception.
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Jaime, Xavier A., Jay P. Angerer, Chenghai Yang, John Walker, Jose Mata, Doug R. Tolleson, and X. Ben Wu. "Exploring Effective Detection and Spatial Pattern of Prickly Pear Cactus (Opuntia Genus) from Airborne Imagery before and after Prescribed Fires in the Edwards Plateau." Remote Sensing 15, no. 16 (August 15, 2023): 4033. http://dx.doi.org/10.3390/rs15164033.
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Over the past century, prickly pear (PP) cactus (e.g., genus Opuntia; subgenus Platyopuntia) has increased on semi-arid rangelands. Effective detection of cacti abundance and spatial pattern is challenging due to the inherent heterogeneity of rangeland landscapes. In this study, high-resolution multispectral imageries (0.21 m) were used to test object-based (OB) feature extraction, random forest (RF) machine learning, and spectral endmember (n-D) classification methods to map PP and evaluate its spatial pattern. We trained and tested classification methods using field-collected GPS location, plant cover, and spectrometry from 288 2 m radius polygons before a prescribed burn and 480 samples after the burn within a 69.2-ha burn unit. The most accurate classification method was then used to map PP distribution and quantify abundance before and after fire. As a case study, we assessed the spatial pattern of mapped PP cover, considering topoedaphic setting and burn conditions. The results showed that the endmember classification method, spectral angle mapper (SAM), outperformed the RF and OB classifications with higher kappa coefficients (KC) (0.93 vs. 0.82 and 0.23, respectively) and overall accuracies (OA) (0.96 vs. 0.91 and 0.49) from pre-fire imagery. KC and OA metrics of post-fire imagery were lower, but rankings among classification methods were similar. SAM classifications revealed that fire reduced PP abundance by 46.5%, but reductions varied by soil type, with deeper soils having greater decreases (61%). Kolmogorov-Smirnov tests indicated significant changes before and after fire in the frequency distribution of PP cover within deeper soils (D = 0.64, p = 0.02). A two-way ANOVA revealed that the interaction of season (pre- vs. post-fire) and soils significantly (p < 0.00001) influenced the spatial pattern of PP patches. Fire also reduced the size and shape of PP patches depending on the topoedaphic settings. This study provides an innovative and effective approach for integrating field data collection, remote sensing, and endmember classification methods to map prickly pear and assess the effects of prescribed fire on prickly pear spatial patterns. Accurate mapping of PP can aid in the design and implementation of spatially explicit rangeland management strategies, such as fire, that can help reduce and mitigate the ecological and economic impacts of prickly pear expansion.
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Bi, Xiaoke, Connor Beck, and Yiyang Gong. "Genetically Encoded Fluorescent Indicators for Imaging Brain Chemistry." Biosensors 11, no. 4 (April 11, 2021): 116. http://dx.doi.org/10.3390/bios11040116.
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Genetically encoded fluorescent indicators, combined with optical imaging, enable the detection of physiologically or behaviorally relevant neural activity with high spatiotemporal resolution. Recent developments in protein engineering and screening strategies have improved the dynamic range, kinetics, and spectral properties of genetically encoded fluorescence indicators of brain chemistry. Such indicators have detected neurotransmitter and calcium dynamics with high signal-to-noise ratio at multiple temporal and spatial scales in vitro and in vivo. This review summarizes the current trends in these genetically encoded fluorescent indicators of neurotransmitters and calcium, focusing on their key metrics and in vivo applications.
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Tan, Kun, Zengfu Hou, Donglei Ma, Yu Chen, and Qian Du. "Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint." Remote Sensing 11, no. 13 (July 3, 2019): 1578. http://dx.doi.org/10.3390/rs11131578.
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Hyperspectral imagery contains abundant spectral information. Each band contains some specific characteristics closely related to target objects. Therefore, using these characteristics, hyperspectral imagery can be used for anomaly detection. Recently, with the development of compressed sensing, low-rank-representation-based methods have been applied to hyperspectral anomaly detection. In this study, novel low-rank representation methods were developed for anomaly detection from hyperspectral images based on the assumption that hyperspectral pixels can be effectively decomposed into a low-rank component (for background) and a sparse component (for anomalies). In order to improve detection performance, we imposed a spatial constraint on the low-rank representation coefficients, and single or multiple local window strategies was applied to smooth the coefficients. Experiments on both simulated and real hyperspectral datasets demonstrated that the proposed approaches can effectively improve hyperspectral anomaly detection performance.
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Maynard, Kristen R., Madhavi Tippani, Yoichiro Takahashi, BaDoi N. Phan, Thomas M. Hyde, Andrew E. Jaffe, and Keri Martinowich. "dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues." Nucleic Acids Research 48, no. 11 (May 8, 2020): e66-e66. http://dx.doi.org/10.1093/nar/gkaa312.
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Abstract Multiplex single-molecule fluorescent in situ hybridization (smFISH) is a powerful method for validating RNA sequencing and emerging spatial transcriptomic data, but quantification remains a computational challenge. We present a framework for generating and analyzing smFISH data in complex tissues while overcoming autofluorescence and increasing multiplexing capacity. We developed dotdotdot (https://github.com/LieberInstitute/dotdotdot) as a corresponding software package to quantify RNA transcripts in single nuclei and perform differential expression analysis. We first demonstrate robustness of our platform in single mouse neurons by quantifying differential expression of activity-regulated genes. We then quantify spatial gene expression in human dorsolateral prefrontal cortex (DLPFC) using spectral imaging and dotdotdot to mask lipofuscin autofluorescence. We lastly apply machine learning to predict cell types and perform downstream cell type-specific expression analysis. In summary, we provide experimental workflows, imaging acquisition and analytic strategies for quantification and biological interpretation of smFISH data in complex tissues.
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Hu, Yuan, Lei Chen, Zhibin Wang, Xiang Pan, and Hao Li. "Towards a More Realistic and Detailed Deep-Learning-Based Radar Echo Extrapolation Method." Remote Sensing 14, no. 1 (December 22, 2021): 24. http://dx.doi.org/10.3390/rs14010024.
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Deep-learning-based radar echo extrapolation methods have achieved remarkable progress in the precipitation nowcasting field. However, they suffer from a common notorious problem—they tend to produce blurry predictions. Although some efforts have been made in recent years, the blurring problem is still under-addressed. In this work, we propose three effective strategies to assist deep-learning-based radar echo extrapolation methods to achieve more realistic and detailed prediction. Specifically, we propose a spatial generative adversarial network (GAN) and a spectrum GAN to improve image fidelity. The spatial and spectrum GANs aim at penalizing the distribution discrepancy between generated and real images from the spatial domain and spectral domain, respectively. In addition, a masked style loss is devised to further enhance the details by transferring the detailed texture of ground truth radar sequences to extrapolated ones. We apply a foreground mask to prevent the background noise from transferring to the outputs. Moreover, we also design a new metric termed the power spectral density score (PSDS) to quantify the perceptual quality from a frequency perspective. The PSDS metric can be applied as a complement to other visual evaluation metrics (e.g., LPIPS) to achieve a comprehensive measurement of image sharpness. We test our approaches with both ConvLSTM baseline and U-Net baseline, and comprehensive ablation experiments on the SEVIR dataset show that the proposed approaches are able to produce much more realistic radar images than baselines. Most notably, our methods can be readily applied to any deep-learning-based spatiotemporal forecasting models to acquire more detailed results.
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Higgins, R., S. Kabanovic, C. Pabst, D. Teyssier, J. R. Goicoechea, O. Berne, E. Chambers, et al. "Observation and calibration strategies for large-scale multi-beam velocity-resolved mapping of the [CII] emission in the Orion molecular cloud." Astronomy & Astrophysics 652 (August 2021): A77. http://dx.doi.org/10.1051/0004-6361/202039621.
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Context. The [CII] 158 μm far-infrared fine-structure line is one of the dominant cooling lines of the star-forming interstellar medium. Hence [CII] emission originates in and thus can be used to trace a range of ISM processes. Velocity-resolved large-scale mapping of [CII] in star-forming regions provides a unique perspective of the kinematics of these regions and their interactions with the exciting source of radiation. Aims. We explore the scientific applications of large-scale mapping of velocity-resolved [CII] observations. With the [CII] observations, we investigate the effect of stellar feedback on the ISM. We present the details of observation, calibration, and data reduction using a heterodyne array receiver mounted on an airborne observatory. Methods. A 1.15 square degree velocity-resolved map of the Orion molecular cloud centred on the bar region was observed using the German REceiver for Astronomy at Terahertz Frequencies (upGREAT) heterodyne receiver flying on board the Stratospheric Observatory for Infrared Astronomy. The data were acquired using the 14 pixels of the German REceiver for Astronomy at Terahertz Frequencies that were observed in an on-the-fly mapping mode. 2.4 million spectra were taken in total. These spectra were gridded into a three-dimensional cube with a spatial resolution of 14.1 arcseconds and a spectral resolution of 0.3 km s−1. Results. A square-degree [CII] map with a spectral resolution of 0.3 km s−1 is presented. The scientific potential of this data is summarized with discussion of mechanical and radiative stellar feedback, filament tracing using [CII], [CII] opacity effects, [CII] and carbon recombination lines, and [CII] interaction with the large molecular cloud. The data quality and calibration is discussed in detail, and new techniques are presented to mitigate the effects of unavoidable instrument deficiencies (e.g. baseline stability) and thus to improve the data quality. A comparison with a smaller [CII] map taken with the Herschel/Heterodyne Instrument for the Far-Infrared spectrometer is presented. Conclusions. Large-scale [CII] mapping provides new insight into the kinematics of the ISM. The interaction between massive stars and the ISM is probed through [CII] observations. Spectrally resolving the [CII] emission is necessary to probe the microphysics induced by the feedback of massive stars. We show that certain heterodyne instrument data quality issues can be resolved using a spline-based technique, and better data correction routines allow for more efficient observing strategies.
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Carreño, A., L. Bergamaschi, A. Martínez, D. Ginestar, A. Vidal-Ferràndiz, and G. Verdú. "Strategies of Preconditioner Updates for Sequences of Linear Systems Associated with the Neutron Diffusion." Computational and Mathematical Methods 2022 (June 26, 2022): 1–13. http://dx.doi.org/10.1155/2022/3884836.
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The time-dependent neutron diffusion equation approximates the neutronic power evolution inside a nuclear reactor core. Applying a Galerkin finite element method for the spatial discretization of these equations leads to a stiff semi-discrete system of ordinary differential equations. For time discretization, an implicit scheme is used, which implies solving a large and sparse linear system of equations for each time step. The GMRES method is used to solve these systems because of its fast convergence when a suitable preconditioner is provided. This work explores several matrix-free strategies based on different updated preconditioners, which are constructed by low-rank updates of a given initial preconditioner. They are two tuned preconditioners based on the bad and good Broyden’s methods, initially developed for nonlinear equations and optimization problems, and spectral preconditioners. The efficiency of the resulting preconditioners under study is closely related to the selection of the subspace used to construct the update. Our numerical results show the effectiveness of these methodologies in terms of CPU time and storage for different nuclear benchmark transients, even if the initial preconditioner is not good enough.
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Denzinger, Annette, Marco Tschapka, and Hans-Ulrich Schnitzler. "The role of echolocation strategies for niche differentiation in bats." Canadian Journal of Zoology 96, no. 3 (March 2018): 171–81. http://dx.doi.org/10.1139/cjz-2017-0161.
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Guilds subdivide bat assemblages into basic structural units of species with similar patterns of habitat use and foraging modes, but do not explain mechanisms of niche differentiation. Bats have evolved four different echolocation strategies allowing the access to four different trophic niche spaces differing in niche dimensions. Bats foraging in open and edge spaces use the “aerial hawking or trawling strategy” and detect and localize prey by evaluating pulse–echo trains in which the prey echo is unmasked. The pulse–echo pairs deliver mainly positional information on the prey and only little information on its nature. Signals are highly variable and are adapted for detection and localization in open space and (or) edge space. In narrow space, bats identify prey by solving a pattern recognition task. Bats using the “flutter detecting strategy” evaluate glint pattern in prey echoes; bats using the “active gleaning strategy” evaluate the spectral–temporal pattern of the prey–clutter echo complex; and bats using the “passive gleaning strategy” evaluate the pattern of prey-generated cues to find food and use echolocation only for spatial orientation. The less variable signals of narrow space bats are adapted for pattern recognition. The diverse and species-rich tropical bat assemblage at Barro Colorado Island, Panama, is here used as an exemplar for assigning bats to guilds, and we discuss the role of echolocation and other adaptations for niche differentiation within guilds.
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Paoletti, Mercedes E., and Juan M. Haut. "Adaptable Convolutional Network for Hyperspectral Image Classification." Remote Sensing 13, no. 18 (September 11, 2021): 3637. http://dx.doi.org/10.3390/rs13183637.
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Nowadays, a large number of remote sensing instruments are providing a massive amount of data within the frame of different Earth Observation missions. These instruments are characterized by the wide variety of data they can collect, as well as the impressive volume of data and the speed at which it is acquired. In this sense, hyperspectral imaging data has certain properties that make it difficult to process, such as its large spectral dimension coupled with problematic data variability. To overcome these challenges, convolutional neural networks have been proposed as classification models because of their ability to extract relevant spectral–spatial features and learn hidden patterns, along their great architectural flexibility. Their high performance relies on the convolution kernels to exploit the spatial relationships. Thus, filter design is crucial for the correct performance of models. Nevertheless, hyperspectral data may contain objects with different shapes and orientations, preventing filters from “seeing everything possible” during the decision making. To overcome this limitation, this paper proposes a novel adaptable convolution model based on deforming kernels combined with deforming convolution layers to fit their effective receptive field to the input data. The proposed adaptable convolutional network (named DKDCNet) has been evaluated over two well-known hyperspectral scenes, demonstrating that it is able to achieve better results than traditional strategies with similar computational cost for HSI classification.
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He, Jing, Gang Liu, Weile Li, Chuan Tang, and Jiayan Lu. "An evaluation approach for segmentation results of high-resolution remote sensing images based on the degree distribution of land cover networks." International Journal of Modern Physics B 32, no. 25 (October 8, 2018): 1850283. http://dx.doi.org/10.1142/s0217979218502831.
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Identifying the degree distribution of land cover networks is helpful to find analytical methods for characterizing complex land cover, including segmentation techniques of remote sensing images of land cover. After segmentation, we can obtain the geographical objects and corresponding relationships. In order to evaluate the segmentation results, we introduce the concept of land cover network and present an analysis method based on statistics of its degree distribution. Considering the object-oriented segmentation and objects merge-based spectral difference segmentation, we construct the land cover networks for different segmentation scales and spatial resolutions under these two segmentation strategies, and study the degree distribution of each land cover network. Experimental results indicate that, for the object-oriented segmentation, the degree distributions of land cover networks follow approximately a Poisson distribution, regardless of the segmentation scales and spatial resolutions. For the objects-merge method based on spectral difference segmentation, degree distributions exhibit heavy tails. Compared with all the segmentation results, the pattern spots after objects-merge better retain the integrity of geographical features and the land cover network can reflect more accurately the topological properties of real land cover when the threshold of objects merge is suitable. This study shows that we can evaluate the reliability of segmentation results objectively by analyzing the degree distribution pattern of land cover networks.
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Safiabadi Tali, Seied Ali, and Wei Zhou. "Multiresonant plasmonics with spatial mode overlap: overview and outlook." Nanophotonics 8, no. 7 (July 11, 2019): 1199–225. http://dx.doi.org/10.1515/nanoph-2019-0088.
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AbstractPlasmonic nanostructures can concentrate light and enhance light-matter interactions in the subwavelength domain, which is useful for photodetection, light emission, optical biosensing, and spectroscopy. However, conventional plasmonic devices and systems are typically optimized for the operation in a single wavelength band and thus are not suitable for multiband nanophotonics applications that either prefer nanoplasmonic enhancement of multiphoton processes in a quantum system at multiple resonant wavelengths or require wavelength-multiplexed operations at nanoscale. To overcome the limitations of “single-resonant plasmonics,” we need to develop the strategies to achieve “multiresonant plasmonics” for nanoplasmonic enhancement of light-matter interactions at the same locations in multiple wavelength bands. In this review, we summarize the recent advances in the study of the multiresonant plasmonic systems with spatial mode overlap. In particular, we explain and emphasize the method of “plasmonic mode hybridization” as a general strategy to design and build multiresonant plasmonic systems with spatial mode overlap. By closely assembling multiple plasmonic building blocks into a composite plasmonic system, multiple nonorthogonal elementary plasmonic modes with spectral and spatial mode overlap can strongly couple with each other to form multiple spatially overlapping new hybridized modes at different resonant energies. Multiresonant plasmonic systems can be generally categorized into three types according to the localization characteristics of elementary modes before mode hybridization, and can be based on the optical coupling between: (1) two or more localized modes, (2) localized and delocalized modes, and (3) two or more delocalized modes. Finally, this review provides a discussion about how multiresonant plasmonics with spatial mode overlap can play a unique and significant role in some current and potential applications, such as (1) multiphoton nonlinear optical and upconversion luminescence nanodevices by enabling a simultaneous enhancement of optical excitation and radiation processes at multiple different wavelengths and (2) multiband multimodal optical nanodevices by achieving wavelength multiplexed optical multimodalities at a nanoscale footprint.
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Li, Jian, Luigi Maffei, Aniello Pascale, Massimiliano Masullo, Minqi Lin, and Chi-Kwan Chau. "Road traffic noise informational masking with water sound sequences: From laboratory simulation to field study." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A233. http://dx.doi.org/10.1121/10.0018747.
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Road traffic noise control in urban green space is a big concern for urban designers and public managers. The introduction of water sounds into noisy environment has been proven effective based on the soundscape approach. To extend more effective and applicable strategies for water sound informational masking, the exploration of the spatial settings of virtual water sound playbacks in urban parks were conducted both in the laboratory and field settings. Three different spatial water-sound sequences were added into the virtual noisy environment through an immersive spatial audio system and the real urban green park through the digital audio programming of bluetooth loudspeakers. The mental activities and subjective feelings of two group subjects were evaluated by a portable electroencephalogram (EEG) measurement with a post-doc questionnaire. The better masking effects introduced by the spatial settings of water sounds had been confirmed from the results of more positive emotional feedbacks and more relaxed mental state revealed by the spectral power of alpha band across two experimental conditions. Especially, the two-position switching water sounds brought more attentional network activations. Moreover, more sensory accumulation effects reflected by the mental network activations were observed from the brain activities in the in situ measurement compared to laboratory-setting.
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Singh, Suraj Kumar, Shruti Kanga, and Sudhanshu. "Assessment of Geospatial Approaches Used for Classification of Crops." International Journal of Mathematical, Engineering and Management Sciences 3, no. 3 (September 1, 2018): 271–79. http://dx.doi.org/10.33889/ijmems.2018.3.3-019.
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Harvests distinguishing proof from remotely detected pictures is fundamental because of utilization of remote identifying images as a contribution for rural and monetary arranging by the government and private offices. Accessible satellite sensors like IRS AWIFS, LISS, SPOT 5 and furthermore LANDSAT, MODIS are great wellsprings of multispectral information with various spatial resolutions and Hyperion, Hy-Map, AVIRIS are great wellsprings of hyper-Spectral. The technique for current research is choice of satellite information; utilization of appropriate strategy for arrangement and checking the accuracy. From most recent four decades different specialists have been taking a shot at these issues up to some degree yet at the same time a few difficulties are there like numerous products distinguishing proof, separation of harvests of the same sort this paper gives a general survey of the work done in this vital zone. Multispectral and hyper-spectral images contain spectral data about the crops. Good delicate registering and examination aptitudes are required to order and distinguish the class of enthusiasm from that datasets. Various specialists have worked with supervised and unsupervised arrangement alongside hard classifiers and also delicate processing strategies like fuzzy C mean, support vector machine and they have been discovered distinctive outcomes with various datasets.
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Kaufmann, M., J. Blank, T. Guggenmoser, J. Ungermann, A. Engel, M. Ern, F. Friedl-Vallon, et al. "Retrieval of three-dimensional small scale structures in upper tropospheric/lower stratospheric composition as measured by GLORIA." Atmospheric Measurement Techniques Discussions 7, no. 4 (April 29, 2014): 4229–74. http://dx.doi.org/10.5194/amtd-7-4229-2014.
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Abstract. The three-dimensional quantification of small scale processes in the upper troposphere and lower stratosphere is one of the challenges of current atmospheric research and requires the development of new measurement strategies. This work presents first results from the newly developed Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) obtained during the ESSenCe and TACTS/ESMVal aircraft campaigns. The focus of this work is on the so-called dynamics mode data characterized by a medium spectral and a very high spatial resolution. The retrieval strategy for the derivation of two- and three-dimensional constituent fields in the upper troposphere and lower stratosphere is presented. Uncertainties of the main retrieval targets (temperature, O3, HNO3 and CFC-12) and their spatial resolution are discussed. During ESSenCe, high resolution two-dimensional cross-sections have been obtained. Comparisons to collocated remote-sensing and in-situ data indicate a good agreement between the data sets. During TACTS/ESMVal a tomographic flight pattern to sense an intrusion of stratospheric air deep into the troposphere has been performed. This filament could be reconstructed with an unprecedented spatial resolution of better than 500 m vertically and 20 km × 20 km horizontally.
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Tian, Shuang, Qikai Lu, and Lifei Wei. "Multiscale Superpixel-Based Fine Classification of Crops in the UAV-Manned Hyperspectral Imagery." Remote Sensing 14, no. 14 (July 8, 2022): 3292. http://dx.doi.org/10.3390/rs14143292.
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As an effective approach to obtaining agricultural information, the remote sensing technique has been applied in the classification of crop types. The unmanned aerial vehicle (UAV)-manned hyperspectral sensors provide imagery with high spatial and high spectral resolutions. Moreover, the detailed spatial information, as well as abundant spectral properties of UAV-manned hyperspectral imagery, opens a new avenue to the fine classification of crops. In this manuscript, multiscale superpixel-based approaches are proposed for the fine identification of crops in the UAV-manned hyperspectral imagery. Specifically, the multiscale superpixel segmentation is performed and a series of superpixel maps can be obtained. Then, the multiscale information is integrated into image classification by two strategies, namely pre-processing and post-processing. For the pre-processing strategy, the superpixel is regarded as the minimum unit for image classification, whose feature is obtained by using the average of spectral values of pixels within it. At each scale, the classification is performed on the basis of the superpixel. Then, the multiscale classification results are combined to generate the final map. For the post-processing strategy, the pixel-wise classification is implemented to obtain the label and posterior probabilities of each pixel. Subsequently, the superpixel-based voting is conducted at each scale, and these obtained voting results are fused to generate the multiscale voting result. To evaluate the effectiveness of the proposed approaches, three open-sourced hyperspectral UAV-manned datasets are employed in the experiments. Meanwhile, seven training sets with different numbers of labeled samples and two classifiers are taken into account for further analysis. The results demonstrate that the multiscale superpixel-based approaches outperform the single-scale approaches. Meanwhile, the post-processing strategy is superior to the pre-processing strategy in terms of higher classification accuracies in all the datasets.
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Luo, Wenjun, Long Cheng, Lihong Tong, Wennian Yu, and Chris Mechefske. "Prediction and Analysis of Structural Noise from a U-beam Using the FE-SEA Hybrid Method." PROMET - Traffic&Transportation 30, no. 3 (June 28, 2018): 333–42. http://dx.doi.org/10.7307/ptt.v30i3.2721.
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With urban rail transit noise becoming an increasingly serious issue, accurate and quick analysis of the low to medium frequency spectral characteristics of this noise has become important. Based on the FE-SEA (Finite Element - Statistical Energy Analysis) hybrid method, a vibration prediction model of a U-beam was established using a frequency-dividing strategy. The frequency domain and spatial characteristics of the vibration and structural noise of the U-beam within the 1.25-500 Hz frequency range, when subjected to vertical wheel-rail interaction forces, were analyzed. Compared with other methods described in the literature, the proposed FE-SEA hybrid method improves the calculation efficiency while ensuring better accuracy for a wide frequency range of structural noise and vibration. It was found that the excitation frequencies of the wheel-rail force dominate the spectra of the vibration and structural noise of the U-beam. Therefore, the frequency band containing the excitation frequencies should be the target for noise and vibration reduction when implementing strategies. The results show that the bottom plate contributes the most to the sound pressure level at all prediction points, and therefore should be the focus for noise and vibration reduction.
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Liu, Bing, Kuiliang Gao, Anzhu Yu, Lei Ding, Chunping Qiu, and Jia Li. "ES2FL: Ensemble Self-Supervised Feature Learning for Small Sample Classification of Hyperspectral Images." Remote Sensing 14, no. 17 (August 27, 2022): 4236. http://dx.doi.org/10.3390/rs14174236.
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Classification with a few labeled samples has always been a longstanding problem in the field of hyperspectral image (HSI) processing and analysis. Aiming at the small sample characteristics of HSI classification, a novel ensemble self-supervised feature-learning (ES2FL) method is proposed in this paper. The proposed method can automatically learn deep features conducive to classification without any annotation information, significantly reducing the dependence of deep-learning models on massive labeled samples. Firstly, to utilize the spatial–spectral information in HSIs more fully and effectively, EfficientNet-B0 is introduced and used as the backbone to model input samples. Then, through constraining the cross-correlation matrix of different distortions of the same sample to the identity matrix, the designed model can extract the latent features of homogeneous samples gathering together and heterogeneous samples separating from each other in a self-supervised manner. In addition, two ensemble learning strategies, feature-level and view-level ensemble, are proposed to further improve the feature-learning ability and classification performance by jointly utilizing spatial contextual information at different scales and feature information at different bands. Finally, the concatenations of the learned features and the original spectral vectors are inputted into classifiers such as random forest or support vector machine to complete label prediction. Extensive experiments on three widely used HSI data sets show that the proposed ES2FL method can learn more discriminant deep features and achieve better classification performance than existing advanced methods in the case of small samples.