Hyperspectral Anomaly Detection With Multiscale Attribute and Edge-Preserving Filters

Abstract

In this letter, a novel anomaly detection method is proposed, which can effectively fuse the multiscale information extracted by attribute and edge-preserving filters. The proposed method consists of the following steps. First, multiscale attribute and edge-preserving filters are utilized to obtain multiscale anomaly detection maps. Then, the multiscale detection maps are fused via an averaging approach, and the training samples of the anomalies and background are selected from the fused detection map. Next, the support vector machine classification is performed on the hyperspectral image to obtain an anomaly probability map. Finally, the detection result is obtained by multiplying the fused detection map and the anomaly probability map, followed by an edge-preserving filtering-based postprocessing. Experiments performed on four real hyperspectral data sets demonstrate that the proposed method shows a better detection performance with respect to several state-of-the-art hyperspectral anomaly detection methods.