LAUSR

Recently, evolutionary algorithms (EAs) have shown their promising performance in solving the hyperspectral endmember extraction (EE) task. Despite that, most of the existing EA-based EE algorithms mainly take advantage of the global search capability of evolutionary computation. A few of them focus on the hyperspectral EE task itself, which is a sparse large-scale problem with constraint. To fill the gap, in this article, a global-to-local EA (GL-EA) is proposed, where the global and local search is performed sequentially to extract the endmembers effectively. Specifically, in the first global search stage, two complementary solution generation strategies, including asymmetric flip-based solution generation and spectral angle distance (SAD)-based solution repair, are designed, with which the sparse large-scale search space of hyperspectral EE is fully explored and the endmembers that satisfy the constraint could be achieved. Then, in the second stage, a perturbation-based local search is suggested, which further enhances the quality of the obtained endmembers. In addition, an endmember repetition-based solution selection strategy is also developed for both global and local search stages, by using which good solutions can be selected effectively during the evolution. Experimental results on different hyperspectral datasets demonstrate that when compared with the state-of-the-art EE algorithms, the proposed GL-EA could extract the endmembers with higher quality.