Anomaly Detection in Hyperspectral Imagery Based on PSO Clustering

摘要：

　　In this paper, we propose a novel anomaly targets detection algorithm baesd on information processing method and KRX anomaly detector.It use fully nolinear feature and decrease bands redundancy for hyperspectral imagery.Firstly, the original hyperspectral imagery is clustered by a new clustering method, i.e.k-means clustering of particle swarm optimization.Then, we extract a largest fourth-order cumulant value in every class, and constitute a optimal band subset.Finally, the KRX detector is used on the band subset to get anomaly detection results.The simulation results demonstrate that the proposed PSOC-KRX algorithm outperforms the other algorithm, it is higher precision and lower false alarm rate.

关键词： hyperspectral anomaly detection particle swarm optimization clustering

作者: Baozhi Cheng Zongguang Guo

作者单位: College of Physics and Electricity Information Engineering,Daqing Normal University, Daqing 163712, China

会议类型: 国际会议

会议名称: 4th international Conference,ICSI2013(第4届群体智能国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 184-191

在线出版日期: 2013-06-12（万方平台首次上网日期，不代表论文的发表时间）