Anomaly Detection in Hyperspectral Imagery based on Spectral Gradient and LLE

摘要：

　　The local linear embedding algorithm(LLE) is applied into the anomaly detection algorithm on the basis of the feature analysis of the hyperspectral data.Then,to deal with the problem of declining capacity of identifying the neighborhood caused by the Euclidean distance,an improved LLE algorithm is developed.The improved LLE algorithm selects neighborhood pixels according to the spectral gradient,thus making the anomaly detection more robust to the changes of light and terrain.Experimental results prove the feasibility of using LLE algorithm to solve the anomaly detection problem,and the effectiveness of the algorithm in improving the detection performance.

关键词： hyperspectral manifold learning local linear embedding anomaly detection spectral gradient

作者: Liangliang Wang Zhiyong Li Jixiang Sun

作者单位: School of Electronic Science and Engineering, National University of Defense Technology,Changsha, 410073, Hunan, China

会议类型: 国际会议

会议名称: the Second International Conference on Frontiers of Manufacturing and Design Science(第二届制造与设计科学国际会议(ICFMD 2011))

会议地点: 台湾

会议语种:英文

页码: 720-724

在线出版日期: 2011-12-11（万方平台首次上网日期，不代表论文的发表时间）