Semisupervised Hyperspectral Image Classification With SVM and PSO

摘要：

This paper proposes a novel semisupervised approach to classify hyperspectral image. This method can overcome the limited training samples problem. It combines support vector machine (SVM) and particle swarm optimization (PSO). The new approach exploits the wealth of unlabeled samples for improving the classification accuracy. The method can inflate the original training samples by estimating the labels of the unlabeled samples. The label estimation process is performed by the designed PSO. The effectiveness of the proposed system is carried on a real hyperspectral data set. The experimental results indicate that the classification performance generated by the proposed algorithm is generally competitive.

关键词： semisupervised particle swarm optimization support vector machine data inflation

作者: Hengzhen Gao Mrinal K. Mandal Gencheng Guo Jianwei Wan

作者单位: School of Electronic Science and Engineering, National University of Defense Technology, Changsha, P Department of Electrical and Computer Engineering University of Alberta, Edmonton, AB, Canada T6G 2V

会议类型: 国际会议

会议名称: 2010 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2010)(2010年检测技术与机电自动化国际会议)

会议地点: 长沙

会议语种:英文

页码: 2575-2578

在线出版日期: 2010-03-13（万方平台首次上网日期，不代表论文的发表时间）