A Fast Spatial–Spectral Preprocessing Module for Hyperspectral Endmember Extraction

Abstract

Mixed-pixel decomposition of a hyperspectral image is developed on the basis of extracting unique constituent elements known as endmembers (EMs) and their abundance fraction estimation. Recently, integration of spatial content and spectral information is applied by means of several preprocessing modules (PPs) with the purpose of improving EM extraction (EE) accuracy and decreasing EE time. In this letter, a fast spatial–spectral preprocessing module is proposed, which determines the spectral purity score of pixels located at spatially homogenous regions. These homogenous regions including not spatial border pixels are identified using unsupervised k-means clustering technique and spatial neighborhood system. Afterward, a fraction of homogenous pixels (usually half) with greater spectral purity score is adopted as the best EM candidates for subsequent EEs. This novel PP is examined on synthetic and real AVIRIS data sets, which demonstrates its worthy performance in terms of accuracy and fast computation time.