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In this paper, we present the optical image simulation from a syntheticaperture radar (SAR) data using deep learning based methods. Two models, i.e.,optical image simulation directly from the SAR data and from multi-temporalSARoptical data, are proposed to testify the possibilities. The deep learningbased methods that we chose to achieve the models are a convolutional neuralnetwork (CNN) with a residual architecture and a conditional generativeadversarial network (cGAN). We validate our models using the Sentinel-1 and -2datasets. The experiments demonstrate that the model with multi-temporalSAR-optical data can successfully simulate the optical image, meanwhile, themodel with simple SAR data as input failed. The optical image simulationresults indicate the possibility of SARoptical information blending for thesubsequent applications such as large-scale cloud removal, and optical datatemporal superresolution. We also investigate the sensitivity of the proposedmodels against the training samples, and reveal possible future directions.

Multi-temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation