Research of Anomaly Detection Approaches Based on Feature Fusion in Hyperspectral Imagery
基于特征层融合的高光谱图像异常检测算法研究

An anomaly detection approach based on feature fusion is presented in this paper.All the detection algorithms,aside from anomaly detection,require training pixels of the desired class.Anomaly detection is the detection of scene elements that appear unlikely with respect to a probabilistic feature of the scene.The method needs on prior information,but the result has much false alarm.In this paper,we use low probability detection to fuse the data in feature level;then segment the image and detect anomaly elements.The result eliminates much false alarm and improves the detectability.We apply the method to the data produced by OMIS system and achieve satisfying results.