Hyperspectral Unmixing Based on Local Collaborative Sparse Regression

• Published in: IEEE geoscience and remote sensing letters Vol. 13; no. 5; pp. 631 - 635
• Main Authors: Shaoquan Zhang, Jun Li, Kai Liu, Chengzhi Deng, Lin Liu, Plaza, Antonio
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Collaboration; Hyperspectral imaging; Image color analysis; Libraries; local collaborative sparse regression; Mixture models; Optimization; Satellites; sparse unmixing; spectral unmixing; spectral unmixing; sparse unmixing; local collaborative sparse regression; Hyperspectral imaging
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2016.2527782

Spectral unmixing is an important technique for hyperspectral data exploitation. In order to solve the unmixing problem using a collection of previously available spectral signatures (i.e., a spectral library), sparse unmixing aims at finding the optimal subset of endmembers to represent the pixels in a hyperspectral image. The classic collaborative unmixing globally assumes that all pixels in a hyperspectral scene share the same active set of endmembers. This assumption rarely holds in practice, as endmembers tend to appear localized in spatially homogeneous areas rather than spread over the whole image. To address this limitation, in this letter, we introduce a new strategy to preserve local collaborativity for sparse hyperspectral unmixing. The proposed approach, which is called local collaborative sparse unmixing, considers the fact that endmember signatures generally appear distributed in local spatial regions instead of uniformly distributed throughout the scene. The proposed approach, which includes spatial information in the standard collaborative formulation, has been experimentally validated using both simulated and real hyperspectral data sets.