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End-member extraction based on segmented vertex component analysis in hyperspectral images.

Hyperspectral images collected by a remote sensing hyperspectral imaging instrument have many mixed pixels, due to the limited resolution of image sensors and the complex diversity of nature. End-member extraction is the process that determines the end-members in mixed pixels. The results of traditional methods are inaccurate, due to the spatial complexity and noise of actual hyperspectral image data. This study presents segmented vertex component analysis (SVCA), wherein the relative complexities of hyperspectral images are segmented into a number of relatively simple spatial subsets to reduce the effect of uncorrelated pixels. The end-members are extracted by finding the vertices of the simplex that minimally encloses the hyperspectral image data in each spatial subset, and the inversion abundance is used to identify each major end-member in each subset. Experimental results demonstrate that the proposed method can effectively implement end-member extraction with high accuracy.