%0 Journal Article
%T ANOMALY DETECTION ALGORITHM OF HYPERSPECTRAL IMAGES BASED ON SPECTRAL ANALYSES<br>基于光谱解译的高光谱图像奇异检测算法
%A GU Yan-Feng
%A LIU Ying
%A JIA You-Hu
%A ZHANG Ye
%A <br>谷延锋
%A 刘颖
%A 贾友华
%A 张晔
%J 红外与毫米波学报
%D 2006
%I Science Press
%X Anomaly detection is an important application of hyperspectral images.Aiming at the problems in current researches,a new anomaly detection algorithm based on spectral analyses was proposed for hyperspectral images.The algorithm used spectral analyses technique to effectively separate target information from complicate backgrounds and greatly suppressed background interferences for detection.And those error data obtained after spectral unmixing with background endmembers only include abundant target information and better follow Gaussian distribution.Principal component analysis was used to transform the error data,and local average singularity was defined to select the most effective principal component for anomaly detection according to high-order statistics.Final detection was realized with conventional RX detector.To validate the effectiveness of the proposed algorithm,numerical experiments were conducted on real AVIRIS data.Experimental results show that the proposed algorithm greatly outperforms the conventional RX algorithm.
%K hyperspectral images
%K anomaly detection
%K principal component analysis<br>高光谱图像
%K 奇异检测
%K 主成分分析
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=D3B4F771D1A06062008B4D0A2EF05996&aid=AA29C103AB1C9673&yid=37904DC365DD7266&vid=C5154311167311FE&iid=B31275AF3241DB2D&sid=8143FF92EEF26F96&eid=F18BA6286A889C1C&journal_id=1001-9014&journal_name=红外与毫米波学报&referenced_num=1&reference_num=12