Affinity propagation based memetic band selection on hyperspectral imagery datasets

This paper presents a novel affinity propagation (AP) based memetic band selection method (APMA) for hyperspectral imagery classification. The method incorporates AP based local search and genetic algorithm (GA) based global search to take advantage of both. Particularly, the AP based local search fine-tunes the GA individuals by adding relevant bands and eliminating irrelevant/redundant bands. A comparison study to the filters methods (including ReliefF, AP based method, and FCBF) and the counterpart wrapper GA feature selection on two hyperspectral imagery datasets demonstrates that APMA is capable of attaining competitive or better classification accuracy with fewer selected bands, which suggests APMA searches the band subset space more efficiently and identify better band subsets.