A two-step iterative algorithm for sparse hyperspectral unmixing via total variation

Jin-Ju Wang, Ting-Zhu Huang, Jie Huang and Liang-Jian Deng

Applied Mathematics and Computation, 2021, vol. 401, issue C

Abstract: Sparse hyperspectral unmixing is a hot topic in the field of remote sensing. Its goal is to find an optimal spectral subset, from a large spectral library, to properly model the mixed pixels in hyperspectral images. Sparse unmixing via variable splitting augmented Lagrangian and total variation (SUnSAL-TV) incorporates a TV regularizer into sparse unmixing, achieving a promising unmixing performance. Note that, SUnSAL-TV is solved by the framework of the alternating direction method of multipliers (ADMM). In this paper, we first propose a weighted collaborative sparse unmixing via TV model, named as WCSU-TV, for hyperspectral unmixing. Then a two-step iterative strategy, based on ADMM, is designed to solve the proposed model. Its key idea is to compute the current solution by a linear combination of the results of two previous iterates, instead of only using current solution in classic ADMM. Experiments on simulated and real hyperspectral data illustrate the effectiveness of the proposed approach.

Keywords: Sparse unmixing; Hyperspectral images; Two-step iterative strategy; Total variation (TV); Weighted ℓ2,1 regularization (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:401:y:2021:i:c:s0096300321001077

DOI: 10.1016/j.amc.2021.126059

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