Abstract
In this paper, we present the optical image simulation from 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 SAR-optical 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 state-of-the-art model with simple SAR data as input failed. The optical image simulation results indicate the possibility of SAR-optical information blending for the subsequent applications such as large-scale cloud removal, and optical data temporal super-resolution. We also investigate the sensitivity of the proposed models against the training samples, and reveal possible future directions.
Highlights
The optical data provided by Sentinel-2 has 13 spectral bands from visible, near infrared to short wave infrared spectrum, with a 5-day revisit time at the equator [1]
This essentially concludes that Task A, which describes the optical image simulation from single synthetic aperture radar (SAR) imagery, fails to predict the image
Task B related methods, i.e., MTCNN and MTcGAN, are found to achieve higher values for each index type compared to the baseline
Summary
The optical data provided by Sentinel-2 has 13 spectral bands from visible, near infrared to short wave infrared spectrum, with a 5-day revisit time at the equator [1]. Sentinel-2 is useful in time-series analysis such as land cover changes and damage area detection. Some researchers have reportedly used data from alternative months (previous or ) to composite the data corrupted by clouds [2,3]. These methods remove only small clouds and ignore the changes between monthly data. All above limitations significantly influence the temporal resolution of optical datasets, and the subsequent time-series analysis. In order to increase the temporal resolution, it is necessary to combine other remote sensing data resources, and conduct multi-source data fusion to predict clean Sentinel-2 images
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