Spectral-Spatial Weighted Sparse Regression for Hyperspectral Image Unmixing

• Published in: IEEE transactions on geoscience and remote sensing Vol. 56; no. 6; pp. 3265 - 3276
• Main Authors: Zhang, Shaoquan, Li, Jun, Li, Heng-Chao, Deng, Chengzhi, Plaza, Antonio
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Coefficients; Complexity theory; Correlation; Data processing; Frameworks; Hyperspectral imaging; Libraries; Methods; Pixels; Regression analysis; Remote sensing; Satellites; Sparse matrices; sparse unmixing; Sparsity; Spatial data; spatial information; spatially weighted unmixing; Spectra; Weighting

Spectral unmixing aims at estimating the fractional abundances of a set of pure spectral materials (endmembers) in each pixel of a hyperspectral image. The wide availability of large spectral libraries has fostered the role of sparse regression techniques in the task of characterizing mixed pixels i...