方向自适应曼巴：用于极化合成孔径雷达图像分类的空间-频率双域协同学习
Direction-adaptive Mamba: Spatial-Frequency Dual-Domain Collaborative Learning for PolSAR Image Classification
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中文总结 AI 辅助
该研究针对现有PolSAR曼巴方法缺陷，提出DA-Mamba框架，通过边缘对齐方向自适应扫描、非下采样轮廓波变换及双域协同学习，在三个真实PolSAR数据集上评估，超越现有方法，验证了所提自适应扫描和双域融合设计的有效性。
中文摘要 AI 辅助
深度学习主导极化合成孔径雷达（PolSAR）图像分类，曼巴架构因其线性复杂度和强大的全局建模能力成为有利的骨干网络。然而，现有PolSAR曼巴方法有两个关键缺陷。本文提出DA-Mamba，一种用于PolSAR分类的具有双域协同学习的方向自适应曼巴框架。它配备边缘对齐方向自适应扫描方案，采用非下采样轮廓波变换（NSCT）分离数据，通过双域协同学习模块整合表示以增强特征可辨别性。在三个真实世界PolSAR数据集上评估，DA-Mamba超越现有方法，验证了所提设计的有效性。代码将公开。
英文摘要
Deep learning dominates polarimetric synthetic aperture radar (PolSAR) image classification, with Mamba architectures serving as favorable backbones due to linear complexity and strong global modeling capacity. However, existing PolSAR Mamba methods have two critical flaws: pure spatial processing discards fine-grained edges and textures, and fixed scanning patterns fail to model direction-variant anisotropic scattering and weak boundaries essential for PolSAR physical analysis. This work proposes DA-Mamba, a direction-adaptive Mamba framework with dual-domain collaborative learning for PolSAR classification. Equipped with an edge-aligned direction-adaptive scanning scheme, DA-Mamba captures long-range spatial dependencies and accurate boundary details. It adopts the Non-Subsampled Contourlet Transform (NSCT) to separate PolSAR data into low-frequency global components and multi-directional high-frequency subbands, extracting anisotropic structural features from high-frequency information while preserving global context via low-frequency branches. A dual-domain collaborative learning module further integrates spatial scattering and frequency-domain representations to strengthen feature discriminability. Evaluated on three real-world PolSAR datasets, DA-Mamba surpasses state-of-the-art methods, verifying the efficacy of the proposed adaptive scanning and dual-domain fusion designs. Code will be publicly available.
