关键词: 高光谱图像, 波段选择, K-AP算法

Abstract: In hyperspectral image analysis, band selection is an effective method to reduce the dimension of hyperspectral images. K-Affinity Propagation（K-AP） algorithm is a highly efficient clustering algorithm, which is mainly used in face recognition and data analysis. However, it has not been applied in hyperspectral image analysis. In this paper, the K-AP algorithm is applied to the band selection for hyperspectral images that can be effective for data compression. According to the characteristics of K-AP algorithm, a new similarity matrix is defined based on Kullback-Leibler divergence to measure the band similarity. The K-AP algorithm is used to cluster and select the most representative bands. The experimental results show that the proposed approach generally can get better performance compared with other popular band selection methods.

Key words: hyperspectral image, band selection, K-AP algorithm