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Classification of hyperspectral image based on SVM optimized by a new particle swarm optimization (EI CONFERENCE)

文献类型：会议论文

作者Gao X.; Yu P.; Yu P.
出版日期2012
会议名称2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012
会议地点Nanjing, China
关键词Support Vector Machine (SVM) is used to classify hyperspectral remote sensing image in this paper. Radial Basis Function (RBF) which is most widely used is chosen as the kernel function of SVM. Selection of kernel function parameter is a pivotal factor which influences the performance of SVM. For this reason Particle Swarm Optimization (PSO) is provided to get a better result. In order to improve the optimization efficiency of kernel function parameter firstly larger steps of grid search method is used to find the appropriate rang of parameter. Since the PSO tends to be trapped into local optimal solutions a weight and mutation particle swam optimization algorithm was proposed in which the weight dynamically changes with a liner rule and the global best particle mutates per iteration to optimize the parameters of RBF-SVM. At last a 220-bands hyperspectral remote sensing image of AVIRIS is taken as an experiment which demonstrates that the method this paper proposed is an effective way to search the SVM parameters and is available in improving the performance of SVM classifiers. 2012 IEEE.
收录类别EI
源URL[http://ir.ciomp.ac.cn/handle/181722/34013]  
专题长春光学精密机械与物理研究所_中科院长春光机所知识产出_会议论文
推荐引用方式
GB/T 7714		 Gao X.,Yu P.,Yu P.. Classification of hyperspectral image based on SVM optimized by a new particle swarm optimization (EI CONFERENCE)[C]. 见:2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012. Nanjing, China.

入库方式： OAI收割

来源：长春光学精密机械与物理研究所

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