基于K-AP算法的高光谱图像波段选择方法

在高光谱图像分析领域中,波段选择是一种能有效减少高光谱图像维度的方法。K类仿射传播算法是一种高效的聚类算法,已成功地应用于人脸识别和数据分析等领域,但在高光谱图像分析领域还少有成功的应用。提出将K-AP算法应用于高光谱图像波段选择,对高光谱图像进行有效的数据压缩。针对K-AP算法的特点,基于KullbackLeibler散度定义了新的相似度矩阵,对波段进行度量,再使用K-AP算法进行聚类,选择最有代表性的波段。实验结果表明,与常用的波段选择方法相比,所提出的方法有更好的表现。

Band Selection Based on K-AP Algorithm for Hyperspectral Images

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.