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