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Sentinel-1 Enhanced Resolution Image by Deep Learning with TerraSAR-X

Ao, Dongyang and Dumitru, Corneliu Octavian and Datcu, Mihai (2018) Sentinel-1 Enhanced Resolution Image by Deep Learning with TerraSAR-X. Mapping Urban Areas from Space, 30.-31. Oct. 2018, Frascati, Italy.

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Official URL: http://muas2018.esa.int/

Abstract

To improve the quality of SAR images, we proposed to train a deep neural network with TerraSAR-X. This is done by using a Dialectical Generative Adversarial Network (Dialectical GAN) to generate high-quality SAR images. This method is based on the analysis of hierarchical SAR information and the “Dialectical” structure of GAN frameworks. As a demonstration, a typical example will be shown where a low-resolution SAR image (e.g., a Sentinel-1 image) with large ground coverage is translated into a high-resolution SAR image (e.g., a TerraSAR-X image). Three traditional algorithms are compared and a new algorithm is proposed based on a network framework by combining conditional WGAN-GP (Wasserstein Generative Adversarial Network - Gradient Penalty) loss functions and spatial gamma matrices under the rule of dialectics. Experimental results show that the SAR image translation works very well when we visually compare the results of our proposed method with the selected traditional methods. Translation of Sentinel-1 data to TerraSAR-X image resolution has attracted great interest within the remote sensing community. First, the high resolution of TerraSAR-X generates SAR images rich in information that allow new innovative applications. Second, the wide area coverage of Sentinel-1 images reduces the need for multiple acquisitions, and decreases the demand for high-cost data. Third, it is much easier for researchers to access Sentinel-1 images than TerraSAR-X images because the Sentinel-1 images are freely available, while the TerraSAR-X images are usually commercial. For validation, we used images of urban areas, so we can apply a spatial matrix to extract geometrical arrangement information. Our method learns an adaptive loss function based on the image pairs at hand, and is regularized by the prescribed image style, which makes it applicable to the task of SAR image translation. Based on the advantages of using a GAN, we have achieved very good results with detailed visual effects demonstrating that our method is better than the existing traditional methods being compared in our presentation.

Item URL in elib:https://elib.dlr.de/123114/
Document Type:Conference or Workshop Item (Speech)
Title:Sentinel-1 Enhanced Resolution Image by Deep Learning with TerraSAR-X
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Ao, Dongyangdongyang.ao (at) dlr.deUNSPECIFIED
Dumitru, Corneliu OctavianCorneliu.Dumitru (at) dlr.deUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:30 October 2018
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:SAR, Sentinel-1, TerraSAR-X, GAN
Event Title:Mapping Urban Areas from Space
Event Location:Frascati, Italy
Event Type:international Conference
Event Dates:30.-31. Oct. 2018
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Dumitru, Corneliu Octavian
Deposited On:19 Nov 2018 13:59
Last Modified:22 Nov 2018 09:34

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