DMS2F-HAD: A Dual-branch Mamba-based Spatial–Spectral Fusion Network for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) aims to identify rare and irregular targets in high-dimensional hyperspectral images (HSIs), which are often noisy and unlabeled data. Existing deep learning methods either fail to capture long-range spectral dependencies (e.g., convolutional neural networks) or suffer from high computational cost (e.g., Transformers). To address these challenges, we propose DMS2F-HAD, a novel dual-branch Mamba-based model. Our architecture utilizes Mamba’s linear-time modeling to efficiently learn distinct spatial and spectral features in specialized branches, which are then integrated by dynamic gated fusion mechanism to enhance anomaly localization. Across fourteen benchmark HSI datasets, our proposed DMS2F-HAD not only achieves a state-of-the-art average AUC of 98.78% but also demonstrates superior efficiency with an inference speed 4.6x faster than comparable deep learning methods. The results highlight DMS2F-HAD's strong generalization and scalability, positioning it as a strong candidate for practical HAD applications. The source code is available at https://anonymous.4open.science/r/DMS2F-HAD-45CC.