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arXiv · 2008.01184

Generative Adversarial Networks for Synthesizing InSAR Patches

Philipp Sibler·Yuanyuan Wang·Stefan Auer·Mohsin Ali·Xiao Xiang Zhu
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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.

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Philipp Sibler, Yuanyuan Wang, Stefan Auer, Mohsin Ali, Xiao Xiang Zhu. 2020-08-03. Generative Adversarial Networks for Synthesizing InSAR Patches. https://arxiv.org/abs/2008.01184

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