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Artykuły w czasopismach na temat "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 (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 (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 (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 (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, et al. "Spectral Characteristics of Beached Sargassum in Response to Drying and Decay over Time." Remote Sensing 15, no. 17 (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 (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, et al. "Fusing Spatial Attention with Spectral-Channel Attention Mechanism for Hyperspectral Image Classification via Encoder–Decoder Networks." Remote Sensing 14, no. 9 (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.