Multi-objective multi-verse optimizer based unsupervised band selection for hyperspectral image classification

Abstract

Hyperspectral band selection is one of the efficacious ways to diminish the size of hyperspectral images. The process of selecting a few useful bands will be successful when two fundamental aspects are considered: information abundance and redundancy among the chosen bands. However, selecting the suitable number of bands in an ill-posed classification problem remains challenging. Overcoming this issue, a novel unsupervised multi-objective multi-verse optimizer-based band selection (MOMVOBS) approach is proposed. It explores optimal trade-offs among the different traits of the objective functions namely information richness, less redundancy and the number of bands to be selected. These three objective functions are optimized simultaneously using a multiverse optimizer (MVO) to obtain the best solutions. To evaluate the quality of selected bands, two widely used supervised classifiers are used, such as support vector machine (SVM) and K-nearest neighbour (KNN). Experimental results evidence for the superiority of the proposed approach over the recent multi-objective optimization-based band selection approaches by selecting the highly informative distinct bands that have better classification performance on four benchmark hyperspectral data sets. The proposed MOMVOBS have obtained 79.50% and 71.35% of overall accuracy for SVM and KNN classifier, respectively, in Indian Pines dataset with 10% of band retention, 93.06% and 88.88% of overall accuracy for SVM and KNN classifier, respectively, in Salinas dataset with 10% of band retention, 92.86% and 85.35% of overall accuracy for SVM and KNN classifier, respectively, in Pavia University dataset with 15% band retention, and 92.42% and 85.33% of overall accuracy for SVM and KNN classifier, respectively, in Botswana dataset with 11% band retention. The achievement of higher accuracy at less than 15% bands is significant.