**A Constrained SparseȬRepresentationȬBased SpatioȬTemporal Anomaly Detector for Moving Targets in Hyperspectralȱ ȱ Imagery Sequences**

**Zhaoxu Li †, Qiang Ling †, Jing Wu, Zhengyan Wang and Zaiping Lin \***

College of Electronic Science and Technology, National University of Defense Technology,ȱ ȱ Changsha 410073, China; lizhaoxu@nudt.edu.cn (Z.L.); lingqiang16@nudt.edu.cn (Q.L.);ȱ ȱ jingwu@nudt.edu.cn (J.W.); wangzhengyan@nudt.edu.cn (Z.W.)

**\*** Correspondence: linzaiping@nudt.edu.cn

† These authors contributed equally to this work.

This paper proposed a constrained sparse representation-based spatio-temporal anomaly detection approach which extends AD from the spatial domain to the spatio-temporal domain. It includes a spatial detector to suppress moving background regions and a temporal detector to suppress non-homogeneous background and stationary objects, both of which maintain the effectiveness of the temporal detector for multiple targets in complex motion situations. Moreover, the smoothing and fusion of the spatial and temporal detection maps could adequately suppress background clutter and false alarms on the maps. Experiments conducted on a real dataset and a synthetic dataset showed that the proposed algorithm could accurately detect multiple targets with different velocities and dense targets with the same trajectory and that it also outperforms other state-of-the-art algorithms in high-noise scenarios.

III. **Hyperspectral and Multispectral Fusion (three papers)**

remotesensingȬ12Ȭ02535Ȭv2
