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

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. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

Sparse hyperspectral unmixing is a hot topic in remote sensing, aiming to find an optimal spectral subset to model mixed pixels in hyperspectral images. The SUnSAL-TV method, incorporating a TV regularizer, shows promising unmixing performance and is solved using the ADMM framework. A new weighted collaborative sparse unmixing model (WCSU-TV) and a two-step iterative strategy based on ADMM are proposed in this paper, demonstrating effectiveness in experiments on simulated and real hyperspectral data.