Blind separation of spectral signatures in hyperspectral imagery

Abstract

For the purpose of material identification, methods for exploring hyperspectral images with minimal human intervention have been investigated. Without any prior knowledge, it is extremely difficult to identify or determine how many endmembers in a scene. To tackle this problem, a new spectral unmixing technique, the spectral data explorer (SDE), is presented in the paper. SDE is a hybrid approach combining the optimal parts of fast independent component analysis (FastICA) and noise-adjusted principal components analysis (NAPCA). Experimental results show that SDE is highly efficient for separating significant signatures of hyperspectral images in a blind environment.