SAR-to-Optical Image Translation via an Interpretable Network

SAR-to-Optical Image Translation via an Interpretable Network

Blog Article

Synthetic aperture radar (SAR) is prevalent in the remote sensing field but is difficult to interpret by human visual perception.Recently, SAR-to-optical (S2O) image conversion methods have provided a prospective solution.However, since there is a substantial domain difference between optical and SAR images, they suffer from low image quality and Soup/Cereal Bowl geometric distortion in the produced optical images.Motivated by the analogy between pixels during the S2O image translation and molecules in a heat field, a 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 nonlinear translation Daily Dresses 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 an unstable diffusion state to a 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.