An effective band selection approach for classification in remote sensing imagery

Abstract

Hyperspectral imagery (HSI) is a special imaging form that is characterized by high spectral resolution with up to hundreds of very narrow and contiguous bands which is ranging from the visible to the infrared region. Since HSI contains more distinctive features than conventional images, its computation cost of processing is very high. That’s why; dimensionality reduction is become significant for classification performance. In this study, dimension reduction has been achieved via VNS based band selection method on hyperspectral images. This method is based on systematic change of neighborhood used in the search space. In order to improve the band selection performance, we have offered clustering technique based on mutual information (MI) before applying VNS. The offered combination technique is called MI-VNS. Support Vector Machine (SVM) has been used as a classifier to evaluate the performance of the proposed band selection technique. The experimental results show that MI-VNS approach has increased the classification performance and decrease the computational time compare to without band selection and conventional VNS.