Subfeature Ensemble-Based Hyperspectral Anomaly Detection Algorithm

Abstract

Hyperspectral images (HSIs) have always played an important role in remote sensing applications. Anomaly detection has become a hot spot in HSI processing in recent years. The popular detecting method is to accurately segment anomalies from the background. Informative bands are very important for the accuracy improvement of the detection technology. However, most of the abnormal targets segmentation methods focus on the usage of all the spectral features, thus are easily affected by redundant bands or feature noise. A hyperspectral anomaly detection algorithm based on subfeature ensemble is proposed in this article. The proposed method consists of the following steps. First, the bands of the original HSI are normalized and randomly divided into several subfeature sets according to different proportions. Second, six methods including the prior-based tensor approximation algorithm (PTA), Reed-Xiaoli method, a low-rank and sparse representation method, a low-rank and sparse matrix decomposition-based Mahalanobis distance method, the graph and total variation regularized low-rank representation-based method, and a method based on tensor principal component analysis are applied to detect anomalies on the original HSI, and the method with the best performance is used to obtain an enhanced feature set. Then, the enhanced features and the subfeatures are ensembled iteratively to construct a new dataset. Finally, the PTA method is operated on the dataset with ensemble features to get the final abnormal target results. Six hyperspectral datasets are used in the experiment. Seven methods are employed as comparisons. The results are analyzed from both qualitative and quantitative perspectives. Extensive experimental results illustrate that the proposed method performs best on all datasets.