A Novel Approach to Band Selection for Hyperspectral Image Classification

Abstract

To select a minimal and effective subset from a mass of bands is the key issue of the study on hyperspectral image classification. This paper put forwards a novel band selection algorithm, which combines mutual information-based grouping method and genetic algorithm. The proposed algorithm reduces the computation cost significantly, as well as keeps a better precision. In addition, resampling based on sequential clustering is employed to tackle the imbalanced data issue and improve the classification accuracy of minority classes. Experimental results on the Washington DC Mall data set validate the effectiveness and efficiency of the proposed algorithm.