用物理模型分解声呐图像，分离海底反照率、地形和衰减。
PhysDNet: Physics-Guided Decomposition Network of Side-Scan Sonar Imagery
- 基于朗伯反射模型，分三路解耦声呐图像成分。
- 无需标注即可自监督训练，分解结果保持地质结构稳定。
- 适合海洋测绘与水下目标识别研究者使用。
侧扫声呐（SSS）图像广泛用于海底制图与水下遥感，但其强度受海底反照率、地形高程和声学路径损耗共同影响，导致图像高度依赖观测视角，降低下游分析的鲁棒性。本文提出 PhysDNet，一种基于物理的多分支网络，将 SSS 图像解耦为三个可解释分量：海底反照率、地形高程与传播损耗。通过嵌入朗伯反射模型，网络从这些分量重建声呐强度，实现无需真值标注的自监督训练。实验表明，解耦表征能保持稳定的地质结构，捕捉物理一致的光照与衰减特性，并生成可靠的阴影图。结果证明，物理引导的分解为 SSS 分析提供了稳定且可解释的域，提升了物理一致性与配准、阴影解析等下游任务性能。
原文摘要 · Abstract (English)
Side-scan sonar (SSS) imagery is widely used for seafloor mapping and underwater remote sensing, yet the measured intensity is strongly influenced by seabed reflectivity, terrain elevation, and acoustic path loss. This entanglement makes the imagery highly view-dependent and reduces the robustness of downstream analysis. In this letter, we present PhysDNet, a physics-guided multi-branch network that decouples SSS images into three interpretable fields: seabed reflectivity, terrain elevation, and propagation loss. By embedding the Lambertian reflection model, PhysDNet reconstructs sonar intensity from these components, enabling self-supervised training without ground-truth annotations. Experiments show that the decomposed representations preserve stable geological structures, capture physically consistent illumination and attenuation, and produce reliable shadow maps. These findings demonstrate that physics-guided decomposition provides a stable and interpretable domain for SSS analysis, improving both physical consistency and downstream tasks such as registration and shadow interpretation.
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