Two-Stream Isolation Forest Based on Deep Features for Hyperspectral Anomaly Detection

Abstract

Hyperspectral anomaly detection (HAD) is a challenging task in hyperspectral image processing, which is to capture the anomaly by spectral and spatial information without prior knowledge. Recently, some isolation forest (IF) methods in the HAD are proposed to achieve good accuracy. However, these methods build isolation trees by the global pixels and single band partition, and the way limits the utilization of spectral-spatial information, resulting in suffering from poor performance in detecting hard anomalies. To this end, a novel two-stream isolation forest based on deep features is proposed for HAD in this article, and it fully exploits the spectral and spatial information to enhance the sensitivity of anomalies. Specifically, the IF model combined with the neural network is first introduced for the HAD, by which deep spectral features are utilized to better guide the construction of IFs. Meanwhile, based on spatial characteristics, the model of two-stream IF is proposed, which is global and local weighted IF. Moreover, to further mine spatial information, the morphological attribute filter and Gaussian filter are applied to generate the spatial detection map, and the spatial result and the two-steam IF spectral result are fused through the nonlinear transformation to obtain the final result. Finally, the experimental results on five hyperspectral data sets demonstrate that the proposed method is superior to other state-of-the-art methods.