A Framework of Target Detection in Hyperspectral Imagery Based on Blind Source Extraction

Abstract

A framework based on the blind source extraction (BSE) algorithm is proposed to detect targets in remotely sensed hyperspectral images. The mean square cross prediction error (MSCPE)-based BSE method is used as the kernel algorithm where the autoregressive (AR) parameters of the targets' spectra are utilized as priors. Numerical simulations show that the proposed framework highlights the desired signal, suppresses the backgrounds, and is able to detect the distribution of the target. In the experiments, the data from the Rochester Institute of Technology (RIT) were used to evaluate the framework. The proposed method achieved a better performance in the tradeoff between the PD and the PFA with subpixel target detection compared with the constrained energy minimization (CEM), the adaptive cosine estimator (ACE), the matched filter (MF), the generalized likelihood ratio test (GLRT), the adaptive matched subspace detector (AMSD), and the orthogonal subspace projection (OSP).