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Proceedings Paper

Nonlinear spectral unmixing based on l1-l2 sparse constraint
Author(s): Xuhui Weng; Wuhu Lei; Sheng Luo
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Paper Abstract

Linear mixing model is widely used in hyperspectral images unmixing for its simplicity while the real ground distribution does not satisfy the model. To achieve a high unmixing performance, the nonlinear spectral mixing model should be further discussed. This paper extends the spectral library by adding virtual endmembers based on the second scattering between endmembers to the original spectral library and transforms the nonlinear problem into a linear issue. By introducing the l1 - l2 sparse constraint and enhancing the virtual abundance weight, the optimization problem is solved by alternating direction method of multipliers. Experiments conducted with simulated data sets and real hyperspectral data show that the proposed algorithm is superior to other state-of-the-art methods.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114271T (31 January 2020); doi: 10.1117/12.2551866
Show Author Affiliations
Xuhui Weng, National Univ. of Defense Technology (China)
Wuhu Lei, National Univ. of Defense Technology (China)
Sheng Luo, National Univ. of Defense Technology (China)

Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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