论文标题
用于合成Insar补丁的生成对抗网络
Generative Adversarial Networks for Synthesizing InSAR Patches
论文作者
论文摘要
生成对抗网络（GAN）已被用于光学和实用值SAR强度图像之间的图像翻译任务一定的成功。应用程序包括通过人工贴片生成和自动SAR-Spictic场景匹配来帮助SAR场景及其光学对应物的解释性。人造复合物值的Insar图像堆的合成要求，除了良好的感知质量，更严格的质量指标，例如相位噪声和相干性。本文提供了生成CNN结构的信号处理模型，描述了影响这些质量指标的效果，并介绍了基于流行的深度学习框架的CNN结构的复杂数据的映射方案。
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.
