Generative Adversarial Networks for Synthesizing InSAR Patches

• URL: http://arxiv.org/abs/2008.01184v1
• Date: Mon, 3 Aug 2020 20:51:01 GMT
• Title: Generative Adversarial Networks for Synthesizing InSAR Patches
• Authors: Philipp Sibler, Yuanyuan Wang, Stefan Auer, Mohsin Ali, Xiao Xiang Zhu
• Abstract summary: Generative Adversarial Networks (GANs) have been employed with certain success for image translation tasks between optical and real-valued SAR intensity imagery. 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.
• Score: 15.260123615399035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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