Particle Swarm Optimization-Based Band Selection for Hyperspectral Target Detection

This letter proposes particle swarm optimization (PSO)-based band selection (BS) approach for hyperspectral target detection. Due to lack of training samples in a detection problem, it is more difficult than classification-purposed BS. The objective function, called maximum-submaximum-ratio (MSR) gauging target-background separation, is proposed for target detection during PSO searching. Typical target detectors such as target-constrained interference-minimized filter and adaptive coherence estimator are studied. Experimental results demonstrate that the proposed MSR-based objective function in conjunction with PSO-based searching can select a small band set while yielding similar or even better detection performance than using all the original bands, sequential forward search-based BS, or BS relying on detection map similarity assessment.