期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2014
卷号：XL-2/W3
页码：249-253
DOI：10.5194/isprsarchives-XL-2-W3-249-2014
出版社：Copernicus Publications
摘要：In the most applications in remote sensing, there is no need to use all of available data, such as using all of bands in hyperspectral images. In this paper, a new band selection method was proposed to deal with the large number of hyperspectral images bands. We proposed a Continuous Genetic Algorithm (CGA) to achieve the best subset of hyperspectral images bands, without decreasing Overall Accuracy (OA) index in classification. In the proposed CGA, a multi-class SVM was used as a classifier. Comparing results achieved by the CGA with those achieved by the Binary GA (BGA) shows better performances in the proposed CGA method. At the end, 56 bands were selected as the best bands for classification with OA of 78.5 %.
关键词：Classification; Band Selection; Hyperspectral Image; Continuous Genetic Algorithm; SVM
