Improved Graph-Based Semisupervised Hyperspectral Band Selection

Band selection (BS) is an effective technique of dimensionality reduction in hyperspectral images (HSIs), which can solve the problems of high computational complexity and information redundancy and is helpful for the classification of HSIs. Since many hyperspectral scenes have limited label samples, semisupervised BS methods have attracted much attention. In this paper, an improved graph-based semisupervised BS method is proposed. The within-class and between-class graphs are firstly constructed by utilizing the information of the labeled and unlabeled samples, then Fisher's criteria combined with sparsity regularization (FCS) is designed to obtain the optimal projection matrix, which is used to select the representative bands. The proposed algorithm performs BS while preserving the local manifold structure of data. Experimental results on three hyperspectral data sets show that the proposed BS algorithm has good performance in selecting representative bands for classification.