Multi-objective endmember extraction for hyperspectral images

Endmember extraction is a critical step of spectral unmixing. In this paper, a novel endmember extraction algorithm based on evolutionary multi-objective optimization is proposed for hyperspectral remote sensing images. In the proposed method, endmember extraction is modeled as a multi-objective optimization problem. Then the root mean square error between the original image and its remixed image and the number of endmembers are chosen as two conflicting objective functions, which are simultaneously optimized by particle swarm optimization algorithm to find the best tradeoff solutions. In order to promote diversity and speed up the convergence of the algorithm, a new particle status updating strategy and a novel method for selecting leaders are designed. The experimental results on both simulated and real hyperspectral remote sensing images confirm the performance of the proposed approach over some existing methods.