Results 1 to 10 of about 1,111,541 (211)

Multiobjective Optimized Endmember Extraction for Hyperspectral Image

Remote Sensing, 2017
Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis. It is also a challenging task due to the intrinsic complexity of remote sensing images and the lack of priori knowledge.
Rong Liu, Bo Du, Liangpei Zhang
doaj   +3 more sources

Fast Endmember Extraction for Massive Hyperspectral Sensor Data on GPUs

International Journal of Distributed Sensor Networks, 2013
Hyperspectral imaging sensor becomes increasingly important in multisensor collaborative observation. The spectral mixture problem seriously influences the efficiency of hyperspectral data exploitation, and endmember extraction is one of the key issues ...
Zebin Wu, Shun Ye, Jie Wei, Zhihui Wei, Le Sun, Jianjun Liu   +5 more
doaj   +2 more sources

Incorporating band selection in the spatial selection of spectral endmembers

International Journal of Applied Earth Observations and Geoinformation, 2020
The impact of band selection on endmember selection is seldom explored in the analysis of hyperspectral imagery. This study incorporates the N-dimensional Spectral Solid Angle (NSSA) band selection tool into the Spectral-Spatial Endmember Extraction ...
Yaqian Long, Benoit Rivard, Derek Rogge
doaj   +1 more source

Hyperspectral Unmixing Using Frequency-Adaptive Convolutional-Mamba Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, deep learning (DL) has achieved remarkable progress in hyperspectral unmixing (HU) owing to its powerful feature extraction and modeling capabilities.
Zhuoyi Zhao, Xiang Xu, Chuiyi Deng, Yanyin Guo, Junwei Li, Antonio Plaza   +5 more
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Unsupervised Hyperspectral Unmixing Based on Multi-Faceted Graph Representation and Curriculum Learning

Remote Sensing
Hyperspectral unmixing aims to estimate endmember spectra and their corresponding abundance fractions at the subpixel scale, which is a critical preprocessing step for quantitative analysis of hyperspectral remote sensing imagery.
Ran Liu, Junfeng Pu, Yanru Chen, Yanling Miao, Dawei Liu, Qi Wang   +5 more
doaj   +1 more source

Fractional Impervious Surface Mapping on Multispectral Images With Visible Shadows via a Bundle-Based Sparse Unmixing Model

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Impervious surface abundance (ISA) is an important indicator for monitoring urbanization and environmental disaster management processes. Commonly used spectral unmixing techniques extract ISA in the form of mixed pixels; however, in previous ...
Yanze Liu, Dacheng Li, Wei Xiong, Qijin Han, Lingling Ma   +4 more
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Anomaly-Guided Double Autoencoders for Hyperspectral Unmixing

Remote Sensing
Deep learning has emerged as a prevalent approach for hyperspectral unmixing. However, most existing unmixing methods employ a single network, resulting in moderate estimation errors and less meaningful endmembers and abundances.
Hongyi Liu, Chenyang Zhang, Jianing Huang, Zhihui Wei   +3 more
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Superpixel-Based Endmember Extraction for Hyperspectral Endometriosis Detection

Current Directions in Biomedical Engineering
Hyperspectral Imaging (HSI) is a promising tool for assisting medical diagnostics, as it enables precise tissue characterization and differentiation through detailed spectral analysis.
Patzke Stefan, Wöhler Christian, Thiem Jörg   +2 more
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Unit-Simplex-Inspired Graph Attention Network for Robust Endmember Determination in Hyperspectral Imagery

Remote Sensing
Endmember extraction (EE) in an unmixing chain aims at determining source component spectra constituting pixels from hyperspectral imagery. However, the influence of spectral variability, nonlinear manifolds, and outliers on datasets can seriously ...
Bin Yang, Bin Wang
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The Successive Projection Algorithm (SPA), an Algorithm with a Spatial Constraint for the Automatic Search of Endmembers in Hyperspectral Data

Sensors, 2008
Spectral mixing is a problem inherent to remote sensing data and results in fewimage pixel spectra representing "pure" targets. Linear spectral mixture analysis isdesigned to address this problem and it assumes that the pixel-to-pixel variability in ...
D. M. Rogge, Benoit Rivard, Jinkai Zhang
doaj