Generative Adversarial Networks for Synthesizing InSAR Patches

Conference: EUSAR 2021 - 13th European Conference on Synthetic Aperture Radar
03/29/2021 - 04/01/2021 at online

Proceedings: EUSAR 2021

Pages: 6Language: englishTyp: PDF

Authors:
Sibler, Philipp (Hensoldt Sensors GmbH, Immenstaad, Germany & Signal Processing in Earth Observation, Technical University of Munich (TUM), Munich, Germany)
Wang, Yuanyuan; Ali, Syed Mohsin; Zhu, Xiao Xiang (Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Wessling, Germany & Signal Processing in Earth Observation, Technical University of Munich (TUM), Munich, Germany)
Auer, Stefan (Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Wessling, Germany)

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.