Spectral Feature Selection with Particle Swarm Optimization for Hyperspectral Classification

摘要：

Spectral band selection is a fundamental problem in hyperspectral classification. This paper addresses the problem of band selection for hyperspectral remote sensing image and SVM parameter optimization. We propose an evolutionary classification system based on particle swarm optimization (PSO) to improve the generalization performance of the SVM classifier. The proposed PSO-SVM algorithm is performed to select the best discriminant features and appropriate SVM parameters for hyperspectral remote sensing imagery simultaneously.

关键词： support vector machine (SVM) Particle Swarm Optimization( PSO) Optimization Feature Selection

作者: Jun Li Sheng Ding

作者单位: College of Computer Science and Technology ,Wuhan University of Science and Technology,Wuhan ,P.R.Ch College of Computer Science and Technology ,Wuhan University of Science and Technology.Wuhan ,P.R.Ch

会议类型: 国际会议

会议名称: 2011 International Conference on Opto-Electronics Engineering and Information Science(2011光电电子工程与信息科学国际会议 ICOEIS 2011)

会议地点: 西安

会议语种:英文

页码: 1558-1562

在线出版日期: 2011-12-23（万方平台首次上网日期，不代表论文的发表时间）