Whale optimization-based band selection technique for hyperspectral image classification

Abstract

ABSTRACT Hyperspectral image (HSI) classification is one of the growing research areas in Remote Sensing. The high dimensionality of the data cube and a high correlation among the hyperspectral bands, poses a difficulty in HSI processing, and selection is one of the promising solutions to address the issue. In this research paper, a whale optimization-based band selection is proposed. Initially, the informative bands are retrieved via a search mechanism which resembles the hunting behaviour of the humpback whales. Later, a hybrid filter is proposed to extract the intrinsic and edge preserving spatial features. Finally, the smoothened bands are trained using a non-linear support vector machine for efficient classification. The exploitation and exploration in the local and global search for the selection of whale hyperspectral bands from the search space proved to be useful in achieving the high-quality classification maps. The overall accuracy reported on the three benchmark data sets – Indian Pines, University of Pavia and Salinas are 99.05, 97.88, and 97.56%, respectively. The proposed method accuracy is achieved with 5% fewer bands on Indian Pines, 70% on University of Pavia and 50% on Salinas Datasets over the other competing methods, which show the effectiveness of the whale optimization-based band selection.