A heuristic-based band selection approach to improve classification accuracy in hyperspectral images

Abstract

Variable Neighborhood Search (VNS) is one of the methods, called metaheuristic, which are based on searching the solution space quickly to get optimal or approximately optimal solution. This method is based on the systematically neighborhood change in search area and generally used to achieve the optimal solution in a short time in high dimensional search space. Examining the data including large scale of information such as hyperspectral images and eliminating redundant features (bands) is quite important for computation time and target classification/detection performance. In this study, band selection as a dimension reduction procedure is employed to hyperspectral images using VNS method. Then the classification was done for different selections of the spectral bands with the spectral angle mapper (SAM) and support vector machine (SVM) on hyperspectral Indian Pine image. The experimental results show that the VNS-based dimension reduction algorithm can improve classification performance in high dimensional hyperspectral data.