Robust Multiscale Spectral-Spatial Regularized Sparse Unmixing for Hyperspectral Imagery

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 17; pp. 1269 - 1285
• Main Authors: Wang, Ke, Zhong, Lei, Zheng, Jiajun, Zhang, Shaoquan, Li, Fan, Deng, Chengzhi, Cao, Jingjing, Su, Dingli
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abundance; Abundance estimation error; Algorithms; Biological system modeling; Coefficients; Data mining; Error reduction; Estimation error; Hyperspectral imaging; Imagery; Immunity; Libraries; Mixture models; multiscale; Optimization; Outliers (statistics); Regularization; Remote sensing; Robustness; Smoothness; sparse hyperspectral unmixing; Spatial data; spatial information; spatial regularization; Weighting

With the aid of endmember spectral libraries, sparse unmixing plays a critical role in interpreting hyperspectral remote sensing data. Integrating spatial clues from hyperspectral data into sparse unmixing frameworks is pivotal for enhancing unmixing capabilities. As such, extracting and harnessing...