Beyond Background Feature Extraction: An Anomaly Detection Algorithm Inspired by Slowly Varying Signal Analysis

Abstract

Background feature extraction is an important step in hyperspectral anomaly detection. However, the lack of prior information about anomaly targets and the complex spectral mixture result in a challenge for robust background feature extraction. Can we solve the anomaly detection problem other than with background feature extraction? Relative to anomalies, the background spectral signal is usually stable and slowly varying. In view of this point, slowly varying background analysis is introduced into anomaly detection in this paper. The desired background signals are obtained through a generalized eigenvalue decomposition problem based on the original data and the differential image. The extracted signals are then combined with a Mahalanobis distance metric to construct the detection estimation. Different data processing procedures and signal extraction patterns are respectively formulated to construct different versions of the slowly varying background-signal-based detector. The performances of the proposed methods were validated on both synthetic and real hyperspectral data. The experimental results reveal that the proposed methods outperform the state-of-the-art anomaly detectors, with superior receiver operating characteristic (ROC) curves, area-under-ROC values, and background–target separation. The sensitivity of the relevant parameters was also analyzed in an experimental analysis.