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Open AccessArticle

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

by Wei He * and Naoto Yokoya *
RIKEN Center for Advanced Intelligence Project, RIKEN, Tokyo 103-0027, Japan
*
Authors to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(10), 389; https://doi.org/10.3390/ijgi7100389
Received: 26 July 2018 / Revised: 8 September 2018 / Accepted: 21 September 2018 / Published: 26 September 2018
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. View Full-Text
Keywords: Sentinel; synthetic aperture radar; optical; data simulation; convolutional neural network; generative adversarial network Sentinel; synthetic aperture radar; optical; data simulation; convolutional neural network; generative adversarial network
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He, W.; Yokoya, N. Multi-Temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation. ISPRS Int. J. Geo-Inf. 2018, 7, 389.

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