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Synthetic aperture radar (SAR) is prevalent in the remote sensing field butis difficult to interpret in human visual perception. Recently, SAR-to-optical(S2O) image conversion methods have provided a prospective solution forinterpretation. However, since there is a huge domain difference betweenoptical and SAR images, they suffer from low image quality and geometricdistortion in the produced optical images. Motivated by the analogy betweenpixels 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 FiniteDifference (TFD) residual structure in light of the TFD equation ofthermodynamics, which allows us to efficiently extract inter-domain invariantfeatures and facilitate the learning of the nonlinear translation mapping. Inaddition, we exploit the first law of thermodynamics (FLT) to devise anFLT-guided branch that promotes the state transition of the feature values fromthe unstable diffusion state to the stable one, aiming to regularize thefeature diffusion and preserve image structures during S2O image translation.S2O-TDN follows an explicit design principle derived from thermodynamic theoryand enjoys the advantage of explainability. Experiments on the public SEN1-2dataset show the advantages of the proposed S2O-TDN over the current methodswith more delicate textures and higher quantitative results.

SAR-to-Optical Image Translation via Thermodynamics-inspired Network