Hyperspectral Unmixing Via Nonconvex Sparse and Low-Rank Constraint

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Guxi Wang¹  Ke Guo²  Mingyue Zhang²  Maozhi Wang²  Ling Chen²  Hongwei Han²  Jiaqing Miao³  Si Guo⁴ 

[1] College of Architecture and Environment, Sichuan University, Chengdu, China;Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China;School of Computer Science and Technology, Southwest Minzu University, Chengdu, China;School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, China;

关键词: Hyperspectral images;    joint-sparsity regression;    low-rank representation (LRR);    sparse unmixing;    weighted Schatten $p$ -norm;   

DOI  :  10.1109/JSTARS.2020.3021520

来源: DOAJ

【 摘 要 】

In recent years, sparse unmixing has attracted significant attention, as it can effectively avoid the bottleneck problems associated with the absence of pure pixels and the estimation of the number of endmembers in hyperspectral scenes. The joint-sparsity model has outperformed the single sparse unmixing method. However, the joint-sparsity model might cause some aliasing artifacts for the pixels on the boundaries of different constituent endmembers. To address this shortcoming, researchers have developed many unmixing algorithms based on low-rank representation, which makes good use of the global structure of data. In addition, the high mutual coherence of spectral libraries strongly affects the applicability of sparse unmixing. In this study, adopting combined constraints imposing sparsity and low rankness, a novel algorithm called nonconvex joint-sparsity and low-rank unmixing with dictionary pruning is developed In particular, we impose sparsity on the abundance matrix using the ℓ_2,p mixed norm, and we also employ the weighted Schatten p-norm instead of the convex nuclear norm as an approximation for the rank. The key parameter p is set between 0.4 and 0.6, and a good quality sparse solution is generated. The effectiveness of the proposed algorithm is demonstrated on both simulated and real hyperspectral datasets.

【 授权许可】

Unknown