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

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.