作者: Zhang, YX (Zhang, Yuxiang); Du, B (Du, Bo); Zhang, LP (Zhang, Liangpei); Wang, SG (Wang, Shugen)
来源出版物: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 卷: 54 期: 3 页: 1376-1389 DOI: 10.1109/TGRS.2015.2479299 出版年: MAR 2016
摘要: Anomaly detection is playing an increasingly important role in hyperspectral image (HSI) processing. The traditional anomaly detection methods mainly extract knowledge from the background and use the difference between the anomalies and the background to distinguish them. Anomaly contamination and the inverse covariance matrix problem are the main difficulties with these methods. The low-rank and sparse matrix decomposition (LRaSMD) technique may have the potential to solve the aforementioned hyperspectral anomaly detection problem since it can extract knowledge from both the background and the anomalies. This paper proposes an LRaSMD-based Mahalanobis distance method for hyperspectral anomaly detection (LSMAD). This approach has the following capabilities: 1) takes full advantage of the LRaSMD technique to set the background apart from the anomalies; 2) explores the low-rank prior knowledge of the background to compute the background statistics; and 3) applies the Mahalanobis distance differences to detect the probable anomalies. Extensive experiments were carried out on four HSIs, and it was found that LSMAD shows a better detection performance than the current state-of-the-art hyperspectral anomaly detection methods.
03.25
学校第十届党委第一轮巡视第四巡视组进驻测绘遥感信息工程国家重点实验室党委
03.19
重庆市测绘科学技术研究院来全重调研座谈
03.16
刘进副教授课题组傅里叶级数目标检测成果在TPAMI上发表
学校召开第十届党委第一轮巡视迎巡工作布置会
03.14
歌颂巾帼志,携手绽芳华——实验室开展系列活动庆祝“三八”国际劳动妇女节
03.06
李必军教授团队联合东风悦享在智能驾驶环境感知领域的新成果发表于CVPR和TGRS
03.03
【珞珈风华】马盈盈:科研报国显担当,巾帼力量谱新篇