Subtractive clustering and phase correlation similarity measure for endmember extraction

Abstract

Target identification using Remote Sensing techniques saves time, cost and reduces difficulties in field investigation. The endmember is a reference spectral response of a pure pixel in the hyperspectral image and is used for object identification/classification from hyperspectral data. Quality of endmembers selected influences classification accuracy. Though there have been several algorithms proposed for endmember extraction, choosing a benchmark algorithm requires further investigation. To the best of our knowledge, similarity measures have not been explored much in the extraction of spectrally distinct signatures called endmembers. In this paper, we propose a similarity measures based subtractive clustering algorithm (SM-SCA) for endmember extraction. The objective of this paper is to explore the applicability of a SM-SCA and effectiveness of different similarity measures in endmember extraction and to compare it's performance with classical endmember extraction algorithms. Implementation on airborne hyperspectral (Samson data and AVIRIS data over Cuprite region) and synthetic data proves that SM-SCA is capable of extracting endmembers of all the materials identified in the data, with appropriate similarity measure. Experimental results show that (i) the similarity measures are potential not only to discriminate but also in extraction of different endmember signatures and (ii) the proposed SM-SCA with phase correlation similarity measure perform comparable to the classical endmember extraction algorithms in identifying endmembers. © 2020 Elsevier B.V.