SDF-Net: Structure-Aware Disentangled Feature Learning for Opticall-SAR Ship Re-identification
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.
69b7fb0891a28162b233fe53
Furui Chen
claimed_verified
2026-03-16T13:07:58.293Z
67ee23d65e1b098a5e89a038
https://cdn-avatars.huggingface.co/v1/production/uploads/67ee23d65e1b098a5e89a038/RsCj7VYHheNTJLCpVo1Gw.jpeg
Chenfree
Chenfree233
user
Chenfree233
69b7fb0891a28162b233fe54
Han Wang
69b7fb0891a28162b233fe55
Yuhan Sun
69b7fb0891a28162b233fe56
Jianing You
69b7fb0891a28162b233fe57
Yixuan Lv
69b7fb0891a28162b233fe58
Zhuang Zhou
69b7fb0891a28162b233fe59
Hong Tan
69b7fb0891a28162b233fe5a
Shengyang Li
SDF-Net: Structure-Aware Disentangled Feature Learning for Opticall-SAR Ship Re-identification
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 is a structure-aware disentangled feature learning network that incorporates geometric consistency constraints into optical-SAR ship re-identification, using a ViT backbone to extract scale-invariant gradient energy statistics and disentangle modality-invariant and modality-specific features.
ViT backbone structure consistency constraint scale-invariant gradient energy statistics disentangled feature learning modality-invariant identity features modality-specific characteristics additive residual fusion