Real-time processing algorithms for target detection and classification inhyperspectral imagery

In this paper, we present a linearly constrained minimum variance (LCMV) be amforming approach to real time processing algorithms for target detection and classification in hyperspectral imagery. The only required knowledge fo r these LCMV-based algorithms is targets of interest, The idea is to design a finite impulse response (FIR) filter to pass through these targets using a set of linear constraints while also minimizing the variance resulting f rom unknown signal sources. Two particular LCMV-based target detectors, the constrained energy minimization (CEM) and the target-constrained interfere nce-minimization filter (TCIMF), are presented. In order to expand the abil ity of the LCMV-based target detectors to classification, the LCMV approach is further generalized so that the targets can be detected and classified simultaneously. By taking advantage of the LCMV-based filter structure, the LCMV-based target detectors and classifiers can be implemented by a QR-dec omposition and be processed line-by-line in real time. The experiments usin g HYDICE and AVIRIS data are conducted to demonstrate their real time imple mentation.