Spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints

Abstract

Hyperspectral sparse unmixing, an image processing technique, leverages a spectral library enriched with endmember spectral information as a prerequisite. It decomposes the hyperspectral data to ascertain the abundance corresponding to each endmember in the spectral library. Currently, the majority of sparse unmixing methods are inadequate in high-noise environments due to their failure to comprehensively account for hyperspectral characteristics. Addressing this challenge, this paper introduces an innovative sparse unmixing approach for hyperspectral images named spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints (SWSU-ATVLR). Initially, the sparse unmixing algorithm is introduced in detail. Subsequently, we present the our method. This method seamlessly integrates the low-rank, adaptive TV and spectral weighting characteristics of hyperspectral data. While preserving the low-rank attributes and sparsity of abundance, the adaptively adjusted abundance matrix exhibits a regularized horizontal and vertical difference ratio across various structures and fully utilizes spectral information, enhancing denoising efficiency. Subsequently, the ADMM algorithm is employed to solve the new model. To validate our proposed algorithm, SWSU-ATVLR method is compared and analysed in detailed experiments with several current state-of-the-art methods through simulated and real data experiments. Experimental results prove that our proposed method is superior to these state-of-the-art methods.