Generative Adversarial Networks for Synthesizing InSAR Patches [article]

Philipp Sibler, Yuanyuan Wang, Stefan Auer, Mohsin Ali, Xiao Xiang Zhu
2020 arXiv   pre-print
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.
more » ... 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.
arXiv:2008.01184v1 fatcat:jojukmp7bfg6lcdvpjk2dxxsvq