Synthetic aperture radar (SAR) is prevalent in the remote sensing field but is difficult to interpret in human visual perception. Recently, SAR-to-optical (S2O) image conversion methods have provided a prospective solution for interpretation. However, since there is a huge domain difference between optical and SAR images, they suffer from low image quality and geometric distortion in the produced optical images. Motivated by the analogy between pixels during the S2O image translation and molecules in a heat field, Thermodynamics-inspired Network for SAR-to-Optical Image Translation (S2O-TDN) is proposed in this paper. Specifically, we design a Third-order Finite Difference (TFD) residual structure in light of the TFD equation of thermodynamics, which allows us to efficiently extract inter-domain invariant features and facilitate the learning of the nonlinear translation mapping. In addition, we exploit the first law of thermodynamics (FLT) to devise an FLT-guided branch that promotes the state transition of the feature values from the unstable diffusion state to the stable one, aiming to regularize the feature diffusion and preserve image structures during S2O image translation. S2O-TDN follows an explicit design principle derived from thermodynamic theory and enjoys the advantage of explainability. Experiments on the public SEN1-2 dataset show the advantages of the proposed S2O-TDN over the current methods with more delicate textures and higher quantitative results.

翻译：合成孔径雷达（SAR）在遥感领域应用广泛，但其图像难以通过人类视觉感知进行解译。近期，SAR到光学（S2O）图像转换方法为解译问题提供了前瞻性解决方案。然而，由于光学与SAR图像之间存在巨大的域差异，生成的图像常出现质量低下和几何畸变问题。受S2O图像翻译中像素行为与热场中分子运动相似性的启发，本文提出面向SAR到光学图像翻译的热力学启发网络（S2O-TDN）。具体而言，基于热力学三阶有限差分（TFD）方程，我们设计了TFD残差结构，可高效提取跨域不变特征并促进非线性翻译映射的学习。此外，利用热力学第一定律（FLT），我们构建了FLT引导分支，推动特征值从不稳定扩散状态向稳定状态转换，从而在S2O图像翻译过程中正则化特征扩散并保持图像结构。S2O-TDN遵循源自热力学理论的显式设计原则，具备可解释性优势。在公开SEN1-2数据集上的实验表明，所提出的S2O-TDN相比现有方法能生成更精细的纹理，并取得更优的定量结果。