Generative Adversarial Networks for Synthesizing InSAR Patches
This work addresses the challenge of generating high-quality InSAR data for applications such as SAR-optical scene matching, but it appears incremental as it builds on existing GAN methods for image translation.
The paper tackles the problem of synthesizing complex-valued InSAR image stacks using Generative Adversarial Networks (GANs), focusing on stringent quality metrics like phase noise and coherence, and presents a signal processing model and mapping scheme for this task.
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.