A new spatial sparsity-based method for extracting endmember spectra from hyperspectral data with some pure pixels

Remote sensing hyperspectral sensors typically collect data in contiguous narrow bands (up to several hundred bands) in the electromagnetic spectrum. In hyperspectral imagery, pixels are often linear mixtures of pure materials (endmembers) contained in the observed scene. In this paper, we propose a new unsupervised spatial method (called 2D-VM) for endmember spectra extraction from data to be collected by future higher spatial resolution hyperspectral sensors, which will allow the existence of some pure pixels. This method is related to the Blind Mixture Identification (BMI) problem, and is based on Sparse Component Analysis (SCA). It extracts the endmember spectra by using a spatial variance-based SCA method, which detects a few pure-pixel zones. Experiments based on synthetic but realistic data are performed to compare the performance of the proposed approach and of methods from the literature. We show that our approach outperforms all other methods.