Hyperspectral endmember extraction using Pearson's correlation coefficient
by Dharambhai Shah; Tanish Zaveri
International Journal of Computational Science and Engineering (IJCSE), Vol. 24, No. 1, 2021

Abstract: Hyperspectral unmixing is a source separation problem. The spectral unmixing process is simply the composition of the three-step chain (subspace identification, endmember extraction and abundance estimation). A critical step in this chain is endmember extraction which finds endmembers from the image for the estimation of abundances. In this paper, a novel framework is proposed which uses the concept of Pearson's correlation coefficient and convex geometry. The novel framework extracts endmembers from the convex set of the two bands extracted using Pearson's correlation coefficient so it is named as Pearson's correlation coefficient-based convex geometry for endmember extraction (PCGE). This PCGE framework is different from other commonly used frameworks due to only two bands convex geometry. Due to only two bands, the computation time for the proposed framework is less. The proposed framework is applied to a synthetic dataset and four popular real hyperspectral datasets. In the simulation results, the proposed framework is compared with other popular frameworks based on standard evaluation parameters (spectral angle error, spectral information divergence and normalised cross-correlation). It has been observed from the simulation results that the proposed framework outperforms popular frameworks. It has been also observed that the proposed framework takes less time than others for extracting endmembers.

Online publication date: Mon, 15-Mar-2021

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