Hyperspectral Anomaly Detection Using Dual-Branch Network Based on Frequency Domain Learning

Existing deep learning-based hyperspectral anomaly detection methods often overlook frequency domain features, hindering the ability to effectively distinguish between background and anomalies. Furthermore, many methods directly apply Mahalanobis distance to reconstructed hyperspectral image (HSI) for detection, disregarding the structural features of the original HSI. This leads to insufficient representation of important information and ultimately limits detection performance. To resolve these challenges, this article presents a dual-branch network based on frequency domain learning. Using the Haar wavelet transform, the original HSI is divided into high- and low-frequency components. Based on the edge detailed properties of the high-frequency component and the smoothness of the low-frequency component, distinct network branches are designed for feature extraction, with the extracted features fused for HSI reconstruction. Notably, the detection process utilizes the outputs of the high- and low-frequency branches directly, rather than the reconstructed HSI. To fully leverage the key structural information in the original HSI, a Mahalanobis distance detection method incorporating the structural similarity index is proposed. By weighting the covariance matrix, the method enhances critical structural and spectral features, improving detection accuracy while effectively suppressing noise. Experiments on six datasets demonstrate the proposed method’s superiority and robustness over eight advanced hyperspectral anomaly detection methods.