[Paper Review] Generative Adversarial Networks for Synthesizing InSAR Patches

Philipp Sibler, Yuanyuan Wang+3 more|arXiv (Cornell University)|Aug 3, 2020

Generative Adversarial Networks and Image Synthesis9 references4 citations

TL;DR

This paper proposes a novel Nyquist mapping scheme to enable complex-valued InSAR patch synthesis using standard real-valued Convolutional Neural Networks (CNNs) in a conditional GAN framework. By modeling the signal processing behavior of CNNs and optimizing loss functions for phase coherence, the method achieves high perceptual quality with low phase noise, enabling synthetic InSAR data generation for training and data augmentation.

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.

Motivation & Objective

• Address the lack of effective methods for generating realistic complex-valued InSAR image patches with high phase coherence.
• Overcome the challenge of adapting real-valued deep learning frameworks to handle complex-valued SAR data in GAN architectures.
• Develop a signal processing model of CNNs to understand and control phase and amplitude distortions during image synthesis.
• Propose a new mapping scheme—Nyquist mapping—that enables direct reuse of real-valued CNN implementations for complex-valued tensors without architectural changes.
• Optimize loss functions to promote phase coherence between real and synthesized InSAR image patches for improved fidelity in interferometric applications.

Proposed method

• Formulate a simplified monofrequency InSAR imaging model assuming flat Earth, constant reflectivity, and linear phase response to define the target signal characteristics.
• Model CNNs as layered filter banks with trainable kernels, bias terms, and nonlinear activation functions (e.g., ReLU), enabling analysis of signal transformations through the network.
• Introduce the Nyquist mapping scheme, which maps complex-valued tensors to real-valued tensors by interleaving real and imaginary components in a way that preserves complex structure and enables compatibility with standard real-valued CNNs.
• Implement conditional GANs where the generator takes a source image (e.g., intensity or amplitude) as input and synthesizes complex-valued InSAR patches with consistent phase patterns.
• Design and apply specialized loss functions focused on minimizing phase noise and maximizing phase coherence between real and generated InSAR image pairs.
• Evaluate the method on synthetic monofrequency scenes, real Sentinel-1 InSAR data, and simulated SAR scenes to validate performance across diverse conditions.

Experimental results

Research questions

• RQ1 How can real-valued deep learning frameworks be effectively adapted to generate complex-valued InSAR patches with high fidelity?
• RQ2 What signal processing effects in CNNs most significantly impact phase noise and phase coherence in synthesized InSAR data?
• RQ3 Can a novel mapping scheme enable the reuse of existing real-valued CNN implementations for complex-valued data without architectural modifications?
• RQ4 How effective are the proposed loss functions in promoting phase coherence between real and generated InSAR image patches?
• RQ5 To what extent does the Nyquist mapping scheme outperform existing complex-valued CNN approaches in terms of perceptual quality and phase stability?

Key findings

• The Nyquist mapping scheme successfully enables the use of standard real-valued CNNs for complex-valued InSAR patch generation without modifying the underlying network architecture.
• The signal processing model of CNNs reveals that filter bank behavior and nonlinear activation functions significantly influence phase distortion and noise in generated InSAR data.
• Experimental results show that the proposed method achieves low phase noise and high phase coherence in synthesized InSAR patches, even when trained on limited real data.
• The use of specialized loss functions tailored for phase coherence leads to measurable improvements in the fidelity of generated InSAR stacks compared to baseline GANs.
• The method demonstrates robust performance on both synthetic monofrequency scenes and real Sentinel-1 InSAR data, validating its applicability to real-world remote sensing tasks.
• The approach enables scalable data augmentation for InSAR classifiers by generating diverse, realistic synthetic patches with controlled parameters such as baseline or scene type.