用双分支Mamba模型高效检测高光谱图像中的异常目标
DMS2F-HAD: A Dual-branch Mamba-based Spatial-Spectral Fusion Network for Hyperspectral Anomaly Detection
- 采用双分支Mamba结构分别处理空间与光谱特征
- 平均AUC达98.78%，推理速度比同类方法快4.6倍
- 适合需要快速精准检测的遥感异常识别场景
高光谱异常检测（HAD）旨在从高维、噪声大且无标签的高光谱图像（HSI）中识别稀有异常目标。现有深度学习方法或难以捕捉长程光谱依赖（如卷积神经网络），或计算成本过高（如Transformer）。为此，本文提出DMS2F-HAD，一种基于双分支Mamba的新模型。其架构利用Mamba的线性时间建模能力，在独立分支中高效学习空间与光谱特征，并通过动态门控融合机制实现特征整合，提升异常定位精度。在14个基准HSI数据集上，DMS2F-HAD不仅达到98.78%的平均AUC，性能领先于现有方法，且推理速度比同类模型快4.6倍。结果表明该模型具备强泛化性与可扩展性，适用于实际HAD应用。
原文摘要 · Abstract (English)
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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