Next Article in Journal
The Effects of Land Use and Land Cover Geoinformation Raster Generalization in the Analysis of LUCC in Portugal
Previous Article in Journal
Identifying Urban Neighborhood Names through User-Contributed Online Property Listings
Previous Article in Special Issue
Feature Extraction and Selection of Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2018, 7(10), 389; https://doi.org/10.3390/ijgi7100389

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

RIKEN Center for Advanced Intelligence Project, RIKEN, Tokyo 103-0027, Japan
*
Author to whom correspondence should be addressed.
Received: 26 July 2018 / Revised: 8 September 2018 / Accepted: 21 September 2018 / Published: 26 September 2018
Full-Text   |   PDF [7589 KB, uploaded 26 September 2018]   |  

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. 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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

He, W.; Yokoya, N. Multi-Temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation. ISPRS Int. J. Geo-Inf. 2018, 7, 389.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top