A Fast Clonal Selection Algorithm for Feature Selection in Hyperspectral Imagery  

高光谱影像特征选择的快速克隆选择算法(英文)

作　　者：钟燕飞 张良培 

机构地区：[1]State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University

出　　处：《Geo-Spatial Information Science》2009年第3期172-181,共10页地球空间信息科学学报（英文）

基　　金：Supported by the Major State Basic Research Development Program (973 Program) of China (No. 2009CB723905);the National High Technology Research and Development Program (863 Program) of China (Nos.2009AA12Z114, 2007AA12Z148, 2007AA12Z181);the National Natural Sci-ence Foundation of China(Nos. 40771139,40523005, 40721001);the Research Fund for the Doctoral Program of Higher Education of China(No.200804861058);the Foundation of National Laboratory of Pattern Recognition

摘　　要：Clonal selection feature selection algorithm （CSFS） based on clonal selection algorithm （CSA）, a new computational intelligence approach, has been proposed to perform the task of dimensionality reduction in high-dimensional images, and has better performance than traditional feature selection algorithms with more computational costs. In this paper, a fast clonal selection feature selection algorithm （FCSFS） for hyperspectral imagery is proposed to improve the convergence rate by using Cauchy mutation instead of non-uniform mutation as the primary immune operator. Two experiments are performed to evaluate the performance of the proposed algorithm in comparison with CSFS using hyperspectral remote sensing imagery acquired by the pushbroom hyperspectral imager （PHI） and the airborne visible/infrared imaging spectrometer （AVlRIS）, respectively. Experimental results demonstrate that the FCSFS converges faster than CSFS, hence providing an effective new option for dimensionality reduction of hyperspectral remote sensing imagery.Clonal selection feature selection algorithm (CSFS) based on clonal selection algorithm (CSA), a new computational intelligence approach, has been proposed to perform the task of dimensionality reduction in high-dimensional images, and has better performance than traditional feature selection algorithms with more computational costs. In this paper, a fast clonal selection feature selection algorithm (FCSFS) for hyperspectral imagery is proposed to improve the convergence rate by using Cauchy mutation instead of non-uniform mutation as the primary immune operator. Two experiments are performed to evaluate the performance of the proposed algorithm in comparison with CSFS using hyperspectral remote sensing imagery acquired by the pushbroom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS), respectively. Experimental results demonstrate that the FCSFS converges faster than CSFS, hence providing an effective new option for dimensionality reduction of hyperspectral remote sensing imagery.

关 键 词：HYPERSPECTRAL feature selection artificial immune systems artificial intelligence 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]