New Divide and Conquer Method on Endmember Extraction Techniques

Abstract

In hyperspectral imagery, endmember extraction (EE) is a main stage in hyperspectral unmixing process where its role lies in extracting distinct spectral signature, endmembers, from hyperspectral image which is considered as the main input for unsupervised hyperspectral unmixing to generate the abundance fractions for every pixel in hyperspectral data. EE process has some difficulties. There are less distinct endmembers than its mixed background; also, there are endmembers that have rare occurrences in data that are considered as difficulties in EE process. In this paper, we propose a new technique that uses divide and conquer method for EE process to find out these difficult (rare or less distinct) endmembers. divide and conquer method is used to divide hyperspectral data scene to multiple divisions and take each division as a standalone scene to enable endmember extraction algorithms (EEAs) to extract difficult endmembers easily and finally conquer all extracted endmembers from all divisions. We implemented this method on real dataset using three EEAs: ATGP, VCA, and SGA and recorded the results that outperform the results from usual endmember extraction techniques methods in all used algorithms.