Spectral Reweighting and Spectral Similarity Weighting for Sparse Hyperspectral Unmixing
Sparse unmixing separates the pixel of hyperspectral images into a collection of pure spectral signatures and the associated fractional coefficients with a complete spectral library as a priori, avoiding the drawback of inaccurate extraction of endmember information from the original hyperspectral image. As a state-of-the-art sparse unmixing method, fast multiscale spatial regularization unmixing algorithm (MUA) consists of two procedures, concerning on the approximation image domain and the original domain, respectively. However, it ignores the inter-superpixel correlation of the original domain that each superpixel only involves a small number of spectral signatures, and ignores the spectral variability of the approximate image domain. We address these two issues by introducing two different weighting factors to enhance the unmixing result. The effectiveness of our proposed algorithm is demonstrated by the experimental results on both synthetic and real hyperspectral data. The code and datasets of this letter can be found at https://github.com/wangtaowei11/Unmixing-Algorithm.
National Natural Science Foundation of China["62106044","62172059"] ; Natural Science Foundation of Jiangsu Province[BK20210221]
|WOS Research Area|
Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
|WOS Accession No|
|EI Accession Number|
Approximation algorithms ; Spectroscopy
|ESI Classification Code|
Cited Times [WOS]:1
|Document Type||Journal Article|
|Department||Department of Computer Science and Engineering|
1.School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China
2.Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, China
3.School of Computer Science and Engineering, Southeast University, Nanjing, China
Zhang，Dengyong,Wang，Taowei,Yang，Shujun,et al. Spectral Reweighting and Spectral Similarity Weighting for Sparse Hyperspectral Unmixing[J]. IEEE Geoscience and Remote Sensing Letters,2022,PP(99):1-1.
Zhang，Dengyong,Wang，Taowei,Yang，Shujun,Jia，Yuheng,&Li，Feng.(2022).Spectral Reweighting and Spectral Similarity Weighting for Sparse Hyperspectral Unmixing.IEEE Geoscience and Remote Sensing Letters,PP(99),1-1.
Zhang，Dengyong,et al."Spectral Reweighting and Spectral Similarity Weighting for Sparse Hyperspectral Unmixing".IEEE Geoscience and Remote Sensing Letters PP.99(2022):1-1.
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