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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 technic
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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.
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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 (
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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 decompos
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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 complemen
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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 w
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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 i
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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 previou
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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 sp
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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 usin
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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 (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 (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. The
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Heiden, Uta, Pablo d’Angelo, Peter Schwind, et al. "Soil Reflectance Composites—Improved Thresholding and Performance Evaluation." Remote Sensing 14, no. 18 (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 type
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14

Liu, Zhaohua, Jiangping Long, Hui Lin, et al. "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 (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 acquire
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15

Li, Na, Chengeng Gong, Huijie Zhao, and Yun Ma. "Space Target Material Identification Based on Graph Convolutional Neural Network." Remote Sensing 15, no. 7 (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 containi
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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 (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 gen
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17

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
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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
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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 (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 ex
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Liu, Zhaohua, Jiangping Long, Hui Lin, et al. "Combination Strategies of Variables with Various Spatial Resolutions Derived from GF-2 Images for Mapping Forest Stock Volume." Forests 14, no. 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
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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 (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. Interaur
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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 (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 usi
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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 mea
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Arun, Pattathal Vijayakumar. "SPATIAL ANALYSIS IN PUBLIC HEALTH DOMAIN: AN NLP APPROACH." Geodesy and Cartography 39, no. 4 (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 usin
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Knauer, Uwe, Cornelius Styp von Rekowski, Marianne Stecklina, et al. "Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers." Remote Sensing 11, no. 23 (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 accu
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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 (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)
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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 (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
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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 (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 estima
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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 (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 cro
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Pascual-Venteo, Ana B., Enrique Portalés, Katja Berger, et al. "Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data." Remote Sensing 14, no. 10 (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
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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 (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 determi
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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 (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. Rece
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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
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Jaime, Xavier A., Jay P. Angerer, Chenghai Yang, et al. "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 (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, p
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Bi, Xiaoke, Connor Beck, and Yiyang Gong. "Genetically Encoded Fluorescent Indicators for Imaging Brain Chemistry." Biosensors 11, no. 4 (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 i
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36

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 (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
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Maynard, Kristen R., Madhavi Tippani, Yoichiro Takahashi, et al. "dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues." Nucleic Acids Research 48, no. 11 (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 ro
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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 (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 fidel
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39

Higgins, R., S. Kabanovic, C. Pabst, 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
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40

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
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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 (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
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Paoletti, Mercedes E., and Juan M. Haut. "Adaptable Convolutional Network for Hyperspectral Image Classification." Remote Sensing 13, no. 18 (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 be
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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 (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
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44

Safiabadi Tali, Seied Ali, and Wei Zhou. "Multiresonant plasmonics with spatial mode overlap: overview and outlook." Nanophotonics 8, no. 7 (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 l
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45

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 (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
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46

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 (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 th
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47

Kaufmann, M., J. Blank, T. Guggenmoser, 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 (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 deri
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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 (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
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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 (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
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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 (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 effect
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