用扩散模型快速生成符合物理规律的船舶尾迹图像。
Physics-Informed Diffusion Models for SAR Ship Wake Generation from Text Prompts
- 用物理仿真数据配文本提示训练扩散模型，实现端到端生成。
- 生成的尾迹模式逼真，推理速度比物理模拟快得多。
- 适合需要快速生成船迹图像的海事遥感研究者使用。
通过合成孔径雷达（SAR）图像中的尾迹特征检测船舶存在正受到广泛关注，但标注数据稀缺严重制约了监督学习的应用。传统基于物理的仿真虽可缓解数据不足问题，却速度慢且阻碍端到端学习。本文提出一种新方法：利用物理仿真器生成的数据训练扩散模型，以文本提示形式关联仿真参数。实验表明，该模型能生成逼真的开尔文尾迹图案，推理速度显著优于物理模拟器。结果证明扩散模型在快速、可控的尾迹图像生成方面具有潜力，为海事SAR分析的端到端下游任务开辟新路径。
原文摘要 · Abstract (English)
Detecting ship presence via wake signatures in SAR imagery is attracting considerable research interest, but limited annotated data availability poses significant challenges for supervised learning. Physics-based simulations are commonly used to address this data scarcity, although they are slow and constrain end-to-end learning. In this work, we explore a new direction for more efficient and end-to-end SAR ship wake simulation using a diffusion model trained on data generated by a physics-based simulator. The training dataset is built by pairing images produced by the simulator with text prompts derived from simulation parameters. Experimental result show that the model generates realistic Kelvin wake patterns and achieves significantly faster inference than the physics-based simulator. These results highlight the potential of diffusion models for fast and controllable wake image generation, opening new possibilities for end-to-end downstream tasks in maritime SAR analysis.
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