A method based on the adaptive cuckoo search algorithm for endmember extraction from hyperspectral remote sensing images. Issue 3 (3rd March 2016) - Zhao, Huihui; Jiang, Yangming; Wang, Tuo; Cui, Weihong; Li, Xiaowen
ABSTRACT: In this letter, an endmember extraction approach based on an adaptive cuckoo search (ACS) algorithm (ACSEE) is proposed for hyperspectral remote sensing imagery. In the proposed algorithm, the problem of endmember extraction is transformed into combinatorial optimization of the candidate endmembers. The effectiveness of the cuckoo search algorithm is demonstrated by its good balance between exploitation of Lévy flights and random walk, which leads the algorithm to effectively explore the solution space and locate potential solutions to avoid falling into local optima. Furthermore, to improve the convergence characteristics of the original cuckoo search algorithm, a new strategy combined with historical information is proposed to accelerate the search process by adjusting the step size of the Lévy flights. The results of experiments conducted using simulated data and well-known hyperspectral remote sensing data indicate that the proposed ACS algorithm can be used as an alternative tool to solve the problem of endmember extraction on account of its robust stability and guarantee of optimal performance.