Target-Constrained Particle Swarm Optimization-Based Band Selection for Hyperspectral Target Detection

A large number of spectral bands in hyperspectral data can help to identify ground objects but also bring additional computational burden. To address this issue, this letter proposes a new target-constrained band selection approach with particle swarm optimization (TCPSOBS) to select a more representational band subset with low redundancy for target detection. TCPSOBS obtains the local and global optimal values of the particle swarm in the current iteration process by calculating the fitness function of each particle derived by constrained energy minimization (CEM), and iteratively updates the particle swarm to find the particle with the optimal band subset index. Experiments prove that TCPSOBS can effectively improve the detection accuracy compared to other most advanced methods.