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A PSO-based End-Member Selection Method for Spectral Unmixing of Multispectral Satellite Images

Authors: Ayed Salman, Mahamed G.H. Omran, Andries P Engelbrecht

Abstract:

An end-member selection method for spectral unmixing that is based on Particle Swarm Optimization (PSO) is developed in this paper. The algorithm uses the K-means clustering algorithm and a method of dynamic selection of end-members subsets to find the appropriate set of end-members for a given set of multispectral images. The proposed algorithm has been successfully applied to test image sets from various platforms such as LANDSAT 5 MSS and NOAA's AVHRR. The experimental results of the proposed algorithm are encouraging. The influence of different values of the algorithm control parameters on performance is studied. Furthermore, the performance of different versions of PSO is also investigated.

Keywords: Particle Swarm Optimization, spectral unmixing, End-members selection, multispectral satellite imagery

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333168

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