A Subspace Selection-Based Discriminative Forest Method for Hyperspectral Anomaly Detection

In this article, a new subspace selection-based discriminative forest (SSDF) method is proposed for the anomaly detection of hyperspectral remote sensing imagery. Most of the existing anomaly detection approaches construct a profile of background instances and then identify instances that do not conform to the background materials as anomalies. However, this type of method generally fails to avoid the background contamination caused by abnormal targets. In this case, we borrow from the concept of isolation and propose an isolation-based discriminative forest model which exploits subsampling rather than modeling the background instances. Furthermore, considering that the data volume of a hyperspectral image is usually huge, the proposed discriminative forest model explores a subspace selection process while splitting the leaf nodes of the binary trees to preserve those bands containing crucial abnormal target information and improve the reliability of the tree-splitting criterion. The proposed detector successfully integrates dimensionality reduction and the data-splitting technique to define pixels as anomaly or background. The extensive experimental results obtained with four hyperspectral data sets demonstrate that the proposed SSDF algorithm outperforms the other state-of-the-art algorithms and hence provides a new perspective for the anomaly detection of hyperspectral remote sensing imagery.