SDF-Net：面向光学-SAR舰船重识别的结构感知解耦特征学习网络

摘要

光学与合成孔径雷达(SAR)图像间的跨模态船舶重识别技术，其核心挑战在于被动光学成像与相干主动雷达传感之间存在的显著辐射差异。现有方法主要依赖统计分布对齐或语义匹配，但往往忽略了一个关键物理先验：船舶作为刚性物体，其几何结构在跨传感模态下保持稳定，而纹理外观则高度依赖成像模态。本文提出SDF-Net——一种结构感知解耦特征学习网络，系统性地将几何一致性融入光学-SAR船舶重识别任务。基于ViT主干网络，SDF-Net引入结构一致性约束，通过从中间层提取尺度不变的梯度能量统计量，有效锚定表征以抵抗辐射变化。在终端阶段，该网络将学习到的表征解耦为模态不变的身份特征与模态特定的属性特征，并通过无参数的加性残差融合实现特征集成，显著提升判别能力。在HOSS-ReID数据集上的大量实验表明，SDF-Net持续超越现有最优方法。代码与训练模型已公开于https://github.com/cfrfree/SDF-Net。

English

Cross-modal ship re-identification (ReID) between optical and synthetic aperture radar (SAR) imagery is fundamentally challenged by the severe radiometric discrepancy between passive optical imaging and coherent active radar sensing. While existing approaches primarily rely on statistical distribution alignment or semantic matching, they often overlook a critical physical prior: ships are rigid objects whose geometric structures remain stable across sensing modalities, whereas texture appearance is highly modality-dependent. In this work, we propose SDF-Net, a Structure-Aware Disentangled Feature Learning Network that systematically incorporates geometric consistency into optical--SAR ship ReID. Built upon a ViT backbone, SDF-Net introduces a structure consistency constraint that extracts scale-invariant gradient energy statistics from intermediate layers to robustly anchor representations against radiometric variations. At the terminal stage, SDF-Net disentangles the learned representations into modality-invariant identity features and modality-specific characteristics. These decoupled cues are then integrated through a parameter-free additive residual fusion, effectively enhancing discriminative power. Extensive experiments on the HOSS-ReID dataset demonstrate that SDF-Net consistently outperforms existing state-of-the-art methods. The code and trained models are publicly available at https://github.com/cfrfree/SDF-Net.

SDF-Net：面向光学-SAR舰船重识别的结构感知解耦特征学习网络

SDF-Net: Structure-Aware Disentangled Feature Learning for Opticall-SAR Ship Re-identification

摘要

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