Global Information and Structure Tensor Guided Collaborative Representation for Anomaly Detection

Anomaly detection is susceptible to complex background and interference noise. Local anomaly detection and collaborative representation detection can effectively suppress background. However, they both encounter the problem of window size determination due to unexpectable target size, which will strongly limit their detection performance. On the other hand, some interferences show similar properties with anomalies, namely small, weak and in low probability, which would be wrongly reputed as anomalies and degrade the detection accuracy too. A global information and structure tensor guided collaborative representation detection method is proposed in this article. First, the global and local information are combined into the collaborative representation to obtain a more valuable background dictionary, preventing the big targets from being missed. Second, to address the problem that anomaly targets are susceptible to interference spectra, an improved structure tensor is introduced into the band selection and detection refining. Bands of better spatial representation for anomaly are selected for detecting, and interference spectra are removed from representation residual to refine the detection result. Experiments on the real datasets show that the method performs better than classical and some recently proposed methods in background and interference suppression, and achieves satisfactory anomaly detection precision.