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Hyperspectral band selection based on dual evaluation measures and improved nondominated sorting genetic algorithm

Abstract. A wide variety of swarm intelligence algorithm-based approaches have been recently developed for selecting the near-optimal bands from hyperspectral images (HSIs). However, such methods [including the nondominated sorting genetic algorithm (NSGA)] for HSIs pixel classification are limited by the lack of effective initialization and directional evolution. This research proposed a successive projections algorithm (SPA) and individual repair operation for NSGA (named Sr-NSGA) for band selection. Specifically, Sr-NSGA used the SPA to initialize the population and construct repair sequences that optimize new generated individuals in evolution. Meanwhile, with the guidance of two mutually restricted fitness functions, i.e., average mutual information and classification accuracy, Sr-NSGA searched for the near-optimal band set in an iterative way. Different combinations obtained by SR-NSGA and three effective band selection methods were tested and compared on the Botswana, KSC, and Indian Pines datasets. The results show that Sr-NSGA yielded better performance than the other three methods. Furthermore, support vector machine was used as the classifier for the pixel classification of the Indian Pines dataset to test Sr-NSGA. Experimental result show that Sr-NSGA achieves an overall accuracy of 95.57% and adapts to different classifiers.