Band selection for heterogeneity classification of hyperspectral transmission images based on multi-criteria ranking

Hyperspectral images contain hundreds of continuous spectral bands, which can provide rich information for target detection and image classification. However, high-dimensional data are easy to cause the "Hughes phenomenon", which affects the classification accuracy of images. In this paper, a band selection method based on multi-criteria ranking is proposed for hyperspectral dimensionality reduction. First, the hyperspectral image acquisition system is built to collect the hyperspectral transmission image datasets containing various heterogeneities. Second, the original data are preprocessed and the band selection method proposed is used for data dimensionality reduction. That is, (1) the whole band is divided into subintervals according to the correlation criteria, and then the bands with large information amount and good inter-class separability are selected according to other criteria functions, (2) the bands of each subinterval are combined, and the combined bands are evaluated according to the optimum index factor and Bhattacharyya distance, and (3) the combined bands are ranked and the top-ranked combined bands are selected as the representative bands for image classification. Finally, the support vector machine is used to verify the effectiveness of the representative bands selected by this method. The experimental results show that the proposed method achieves data dimensionality reduction, and the overall classification accuracy can reach 100% on some selected subsets of bands, which has better classification performance than other related band selection methods. In addition, the computational complexity of high-dimensional data can be effectively reduced by band selection, and the average running time is 95.19% less than that of the full bands while ensuring classification accuracy.