计算机工程与应用2019，Vol.55Issue(13)：201-206,6.DOI:10.3778/j.issn.1002-8331.1804-0015
基于K-AP算法的高光谱图像波段选择方法
Band Selection Based on K-AP Algorithm for Hyperspectral Images
摘要
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 algo-rithm 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.关键词
高光谱图像/波段选择/K-AP算法Key words
hyperspectral image/ band selection/ K-AP algorithm分类
计算机与自动化引用本文复制引用
LI Tequan,YANG Zhijing,LING Yongquan,CAI Nian..基于K-AP算法的高光谱图像波段选择方法[J].计算机工程与应用,2019,55(13):201-206,6.基金项目
国家自然科学基金(No.61471132,61372173). （No.61471132,61372173）
