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

• URL: http://arxiv.org/abs/2602.04102v1
• Date: Wed, 04 Feb 2026 00:37:29 GMT
• Title: DMS2F-HAD: A Dual-branch Mamba-based Spatial-Spectral Fusion Network for Hyperspectral Anomaly Detection
• Authors: Aayushma Pant, Lakpa Tamang, Tsz-Kwan Lee, Sunil Aryal,
• Abstract summary: Hyperspectral anomaly detection (HAD) aims to identify rare and irregular targets in high-dimensional hyperspectral images.<n>We propose DMS2F-HAD, a novel dual-branch Mamba-based model.<n>Our architecture utilizes Mamba's linear-time modeling to efficiently learn distinct spatial and spectral features.
• Score: 2.9390578280177153
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral anomaly detection (HAD) aims to identify rare and irregular targets in high-dimensional hyperspectral images (HSIs), which are often noisy and unlabelled 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 a 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 4.6 times faster than comparable deep learning methods. The results highlight DMS2FHAD's strong generalization and scalability, positioning it as a strong candidate for practical HAD applications.

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