Discrimination and identification for subpixel targets in hyperspectral imagery

Spectral measures have been used in material identification and discrimination. They are effective if the spectral signatures are calibrated and not contaminated. However, it may not be true in many real applications, specifically, for mixed pixels and subpixel targets. This paper investigates the issue of discrimination and identification for subpixel targets and further develops sample spectral covariance/correlation matrix-based hyperspectral measures to account for spectral variability within subpixel targets. Two types of measures are of interest and studied, Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures. In order to substantiate the proposed measures, a real data-based comparative analysis is conducted and compared to two spectral similarity measures, spectral angle mapper (SAM) and spectral information divergence (SID) for performance evaluation. The experiments show that both Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures work very effectively and outperformed the SAM and the SID in discrimination and identification for subpixel targets.