arXiv 2603.125882026-05-28cs.CV

SDF-Net：面向光学-SAR船舶重识别的结构感知解耦特征学习

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

Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间利用技术与工程中心）；Key Laboratory of Space Utilization, Chinese Academy of Sciences（中国科学院空间利用重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；School of Software, Beihang University（北航软件学院）

Furui Chen, Han Wang, Yuhan Sun, Jianing You, Yixuan Lv, Zhuang Zhou, Hong Tan, Shengyang Li

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AI总结：

针对光学与SAR图像间辐射差异导致的船舶重识别挑战，提出SDF-Net，通过结构一致性约束和解耦特征学习，实现模态不变的身份特征提取，在HOSS-ReID数据集上达到最优性能。

摘要：

光学与合成孔径雷达（SAR）图像之间的跨模态船舶重识别（ReID）面临根本性挑战，即被动光学成像与相干主动雷达传感之间的严重辐射差异。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的物理先验：船舶是刚性物体，其几何结构在不同传感模态下保持稳定，而纹理外观则高度依赖模态。本文提出SDF-Net，一种结构感知解耦特征学习网络，系统地将几何一致性引入光学-SAR船舶重识别。基于ViT骨干网络，SDF-Net引入结构一致性约束，从中间层提取尺度不变的梯度能量统计量，以稳健地锚定表示对抗辐射变化。在终端阶段，SDF-Net将学习到的表示解耦为模态不变的身份特征和模态特定的特征。然后通过无参数的加性残差融合整合这些解耦线索，有效增强判别能力。在HOSS-ReID数据集上的大量实验表明，SDF-Net持续优于现有最先进方法。代码和训练模型已在https://github.com/cfrfree/SDF-Net公开。

英文摘要：

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.

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