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：2008
卷号：XXXVII Part B7
页码：341-346
出版社：Copernicus Publications
摘要：A new dynamical dimensional reduction model (HDRM) for hyperspectral images is proposed based on clone selection algorithm which is inspired from nature immune system in this paper. In existing dimensional reduction method, feature selection is most inefficient. To improve the efficiency, the feature selection problem in hyperspectral images is taken as a multi-objective optimization problem. The feasible band sets are regarded as antibodies and the evaluation criteria in feature selection are regarded as the antigens in HDRM. Go through generation after generation, the sets keep on evolution under the guidance and constrain of evaluation criteria, ultimately, the optimization sets can be found. The model is trained with a hyperion image data, and the result of feature selection is used in classification to test its effect. It is proved that the time cost in feature selection is 100s in the experiment data, and the iterate time is just 10 when β is 0.631, ω is 0.873
