Hyperspectral (HS) data are enriched with highly resourceful abundant spectral bands. However, analyzing and interpreting these ample amounts of data is a challenging task. Optimal spectral bands should be chosen to address the issue of redundancy and to capitalize on the absolute advantages of HS data. Partial informational correlation (PIC)-based band selection approach is proposed for feature selection-based classification of HS images. PIC measure appears to be more skillful compared to mutual information for estimation of nonparametric conditional dependency. In this proposed approach, HS narrow bands are selected in an innovative way utilizing the PIC. This approach is more efficient in terms of computational time and in generalizing the applicability of selected spectral bands. Further, these optimal spectral bands are used in the support vector machine (SVM) and random forest classifier for performance evaluation. The optimum performance is accomplished with SVM classifier, and the achieved average overall accuracies are 82.89%, 91.4%, and 91.29% for the Indian Pines, Pavia University, and Botswana datasets, respectively. The proposed band selection approach is compared with different state-of-the-art techniques. This methodology improves the classification performances compared to the existing techniques, and the advancement in performances is proven to be statistically significant.
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