Isolation Forest for Anomaly Detection in Hyperspectral Images

A novel anomaly detection method based on Isolation Forest is proposed for hyperspectral images. The method is directly based on a concept that anomalies rather than background can be more susceptible to isolation in the feature space. Based on this idea, the proposed method detects anomalies by directly isolating anomaly pixels from background. First, the probabilities of the anomalies in HSI is first evaluated by global Isolation Forest to obtain an initial anomaly detection map. Then, the initial anomaly detection map is recursively refined with local Isolation Forest so as to make full use of the spatial information. Experimental results on two real hyperspectral data sets demonstrate that the proposed method is superior over several widely used anomaly detection approaches.