Generative adversarial networks for synthesizing InSAR patches

Philipp Sibler, Yuanyuan Wang, Stefan Auer, Syed Mohsin Ali, Xiao Xiang Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationEUSAR 2021 - 13th European Conference on Synthetic Aperture Radar, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1099-1104
Number of pages6
ISBN (Electronic)9783800754571
StatePublished - 2021
Event13th European Conference on Synthetic Aperture Radar, EUSAR 2021 - Virtual, Online, Germany
Duration: 29 Mar 20211 Apr 2021

Publication series

NameProceedings of the European Conference on Synthetic Aperture Radar, EUSAR
Volume2021-March
ISSN (Print)2197-4403

Conference

Conference13th European Conference on Synthetic Aperture Radar, EUSAR 2021
Country/TerritoryGermany
CityVirtual, Online
Period29/03/211/04/21

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