Spectral Reweighting and Spectral Similarity Weighting for Sparse Hyperspectral Unmixing

Zhang，Dengyong1; Wang，Taowei1; Yang，Shujun2; Jia，Yuheng3; Li，Feng1

2022

10.1109/LGRS.2022.3221093

IEEE Geoscience and Remote Sensing Letters   Impact Factor & Quartile

1545-598X

1558-0571

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.

abundance estimation similarity weighting sparse unmixing Spectral weighting

EI ; SCI

English

Others

National Natural Science Foundation of China["62106044","62172059"] ; Natural Science Foundation of Jiangsu Province[BK20210221]

Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology

Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology

WOS:000886934400003

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20224613112893

Approximation algorithms ; Spectroscopy

Mathematics:921

2-s2.0-85141604807

Scopus

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.