Nonlinear spectral unmixing based on l1-l2 sparse constraint

Xuhui Weng; Wuhu Lei; Sheng Luo

doi:10.1117/12.2551866

31 January 2020 Nonlinear spectral unmixing based on l1-l2 sparse constraint

Xuhui Weng, Wuhu Lei, Sheng Luo

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 114271T (2020) https://doi.org/10.1117/12.2551866
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

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.

Citation Download Citation

Xuhui Weng, Wuhu Lei, and Sheng Luo "Nonlinear spectral unmixing based on l1-l2 sparse constraint", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114271T (31 January 2020); https://doi.org/10.1117/12.2551866

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