Abstract
Cross-modal ship re-identification (ReID) between optical and synthetic aperture radar (SAR) imagery has recently emerged as a critical yet underexplored task in maritime intelligence and surveillance. However, the substantial modality gap between optical and SAR images poses a major challenge for robust identification. To address this issue, we propose MOS, a novel framework designed to mitigate the optical-SAR modality gap and achieve modality-consistent feature learning for optical-SAR cross-modal ship ReID. MOS consists of two core components: (1) Modality-Consistent Representation Learning (MCRL) applies denoise SAR image procession and a class-wise modality alignment loss to align intra-identity feature distributions across modalities. (2) Cross-modal Data Generation and Feature fusion (CDGF) leverages a brownian bridge diffusion model to synthesize cross-modal samples, which are subsequently fused with original features during inference to enhance alignment and discriminability. Extensive experiments on the HOSS ReID dataset demonstrate that MOS significantly surpasses state-of-the-art methods across all evaluation protocols, achieving notable improvements of +3.0%, +6.2%, and +16.4% in R1 accuracy under the ALL to ALL, Optical to SAR, and SAR to Optical settings, respectively. The code and trained models will be released upon publication.
Abstract (translated)
光学和合成孔径雷达（SAR）图像之间的跨模态船舶再识别（ReID）最近已成为海事智能与监视领域中的一个关键但研究较少的任务。然而，光学图像和SAR图像之间显著的模态差异对稳健的身份识别构成了重大挑战。为了解决这个问题，我们提出了MOS框架，这是一种旨在减少光-SAR模态差距并实现光-SAR跨模态船舶ReID的一致性特征学习的新方法。 MOS包含两个核心组件： 1. **一致性表示学习（MCRL）**：应用去噪SAR图像处理技术和类别的模态对齐损失来调整同一身份在不同模态下的特征分布，从而实现跨模态内的特征一致。 2. **跨模态数据生成与特性融合（CDGF）**：采用布朗桥扩散模型合成跨模态样本，并在其推理过程中将这些样本与其原始特性相结合以提高一致性与可区分性。 在HOSS ReID数据集上进行的大量实验表明，MOS方法显著超越了所有评估协议下的现有最佳方法，在ALL to ALL、光学到SAR和SAR到光学设置下分别实现了+3.0%、+6.2% 和 +16.4% 的R1准确率提升。发布后的代码与训练好的模型将会公开提供。 该描述展示了MOS框架在解决跨模态船舶再识别挑战方面的创新性和有效性，特别是在填补光学和SAR图像之间差距方面具有显著优势。
URL
https://arxiv.org/abs/2512.03404
