Particle swarm optimization-based band selection for hyperspectral target detection
This paper proposes particle swarm optimization (PSO)-based band selection approach for target detection from hyperspectral imagery. Specifically, typical target detectors such as constrained energy minimization (CEM) and adaptive coherence estimator (ACE) are studied. Due to the lack of training samples in the detection problem, it is more difficult than classification-purposed band selection. Several objective functions are proposed for target detection during PSO searching. In our experiments, we used the PSO with certain criteria to find the best solution for band selection, and show that it can outperform other searching method such as sequential forward search (SFS) in terms of target detection performance.