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.
@article{arxiv.2008.01184,
title = {Generative Adversarial Networks for Synthesizing InSAR Patches},
author = {Philipp Sibler and Yuanyuan Wang and Stefan Auer and Mohsin Ali and Xiao Xiang Zhu},
journal= {arXiv preprint arXiv:2008.01184},
year = {2020}
}
Comments
accepted in preliminary version for EUSAR2020 conference