Classification of hyperspectral images with binary fractional order Darwinian PSO and random forests

A new binary optimization method inspired on the Fractional-Order Darwinian Particle Swarm Optimization is proposed and applied to a novel spectral-spatial classification framework.Afterwards, the new optimization algorithm is used in a novel spectral-spatial classification frameworkfor the selection of the most effective group of bands. In the proposed approach, first, the raw data set (only spectral data) along with the morphological profiles of the first effective principal components are integrated into a stacked vector. Then, the output of this step is considered as the input of the new optimization method. The Random Forest classifier is used as a fitness function for the cross-validation samples and the overall classification accuracy for the evaluation of the group of bands.Finally, the selected bands are classified by a classifier and the output provides the final classification map. Experimental results successfully confirm that the new approach works better than whenconsideringall the raw bands, the whole morphological profile and the combination of the raw bands and morphological profile.