Potential Target Region Extraction and Isolated Forest with Statistical Correlation Representation for Hyperspectral Anomaly Detection

Anomaly detection is a hot topic in the field of hyperspectral image (HSI) processing, which aims to find anomalous targets through spatial and spectral differences from a complex background. However, there still remains two unsolved problems in anomaly detection methods: 1) since the global image covers more complex scenes, it inevitably suffers a lot of false alarms in the anomaly detection and 2) the sensitivity of the target to the global mode or the local mode detector is different. A detector that only considers one mode usually misses some targets, which makes the detection rate low. This article proposes an anomaly detection method for HSIs with potential target region extraction. The potential target region is extracted through the combination of the tensor robust principal component analysis (RPCA) and the curvature circle model. The obtained subregions are used in the region traversing method to filter out the areas not containing the target. In addition, in order to adapt to the different targets contained in the region, the proposed method combines the isolated forest of the global model and the statistical correlation of the local model, thereby enhancing the separability of the target and the background. Experimental results show that the proposed method can effectively extract potential target regions and greatly reduce the false alarm rate. Furthermore, the proposed method can highlight the target for a higher detection rate and accuracy.