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Generative adversarial networks for synthesizing InSAR patches

Sibler, Philipp and Wang, Yuanyuan and Auer, Stefan and Ali, Syed Mohsin and Zhu, Xiao Xiang (2021) Generative adversarial networks for synthesizing InSAR patches. In: Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR. EUSAR 2020, 29. März - 01. April 2021, Leipzig, Germany, ONLINE. ISSN 2197-4403.

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Official URL: https://arxiv.org/abs/2008.01184

Abstract

Generative Adversarial Networks (GANs) have been employed with certain success for image translation tasks between optical and real-valued SAR intensity imagery. Applications include aiding interpretability of SAR scenes with their optical counterparts by artificial patch generation and automatic SAR-optical scene matching. The synthesis of artificial complex-valued InSAR image stacks asks for, besides good perceptual quality, more stringent quality metrics like phase noise and phase coherence. This paper provides a signal processing model of generative CNN structures, describes effects influencing those quality metrics and presents a mapping scheme of complex-valued data to given CNN structures based on popular Deep Learning frameworks.

Item URL in elib:https://elib.dlr.de/138062/
Document Type:Conference or Workshop Item (Speech)
Title:Generative adversarial networks for synthesizing InSAR patches
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Sibler, Philippphilipp.sibler (at) hensoldt.netUNSPECIFIED
Wang, Yuanyuantum, Yuanyuan.Wang (at) dlr.dehttps://orcid.org/0000-0002-0586-9413
Auer, StefanStefan.Auer (at) dlr.dehttps://orcid.org/0000-0001-9310-2337
Ali, Syed MohsinSyed.Ali (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Date:March 2021
Journal or Publication Title:Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
ISSN:2197-4403
Status:Accepted
Keywords:GAN, InSAR, simulation
Event Title:EUSAR 2020
Event Location:Leipzig, Germany, ONLINE
Event Type:international Conference
Event Dates:29. März - 01. April 2021
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Wang, Yuanyuan
Deposited On:01 Dec 2020 15:17
Last Modified:09 Apr 2021 10:41

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