FCMMA: Fourier Conditional Mask-Based Mixed Attention Method for Hyperspectral Anomaly Detection

In recent years, reconstruction-based methods have achieved excellent detection results in the field of hyperspectral anomaly detection (HAD). These methods predominantly operate on two aspects regarding their working principles: 1) reconstructing background pixels and 2) suppressing anomalous pixels. However, most methods only tackle the HAD task from the spatial and spectral domains, making it challenging to effectively suppress anomalies. To eliminate these issues, this article proposes a Fourier conditional mask-based mixed attention (FCMMA) method. First, we propose the FCMMA method for HAD. FCMMA generates a conditional mask (CMASK) that suppresses anomalous high-frequency information and preserves background low-frequency information in the frequency domain, optimizing the anomaly detection process. In addition, to achieve fine-grained HAD, we propose the Fourier anomaly suppression filter (FASF). FASF uses Fourier techniques to manage background and anomalies, improving detection via precise frequency decoupling. Finally, a CMASK network is designed to effectively suppress anomalies. The CMASK network integrated the FASF module and the spatial-spectral multilayer perceptual (SSMLP) machine module together to enhance the transformation and representation capabilities of the generated masks, which can also help suppress anomalies. The results on five different datasets show that the proposed method is more effective and superior when compared to nine state-of-the-art methods.