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.