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In the remotely sensed hyperspectral image, separating pure materials from the mixed pixels and estimating the abundances of each material in the mixing is called hyperspectral unmixing. Hyperspectral unmixing is executed broadly in three steps. The first step is subspace identification which finds the number of endmembers present in the hyperspectral data. The second step is endmember extraction which extracts materials from the data. The last step is abundance estimation which estimates the proportions of each material in the mixing. In these three steps, endmember extraction is very challenging. As per the literature available for endmember extraction, many researchers have used convex geometry to address the endmember extraction. A novel algorithm, Dispersion Index based Endmember Extraction (DIEE) is proposed which applies convex geometry based on the Dispersion Index in this paper. The DIEE algorithm is compared with prevailing algorithms on a real and simulated dataset. It is observed from the simulation that the proposed algorithm is giving a worthy performance on both real datasets and a noisy simulated dataset.

Dispersion Index Based Endmember Extraction for Hyperspectral Unmixing