Dialectical GAN for SAR Image Translation: From Sentinel-1 to TerraSAR-X

TL;DR

This paper introduces Dialectical GAN, a novel deep learning framework that translates low-resolution Sentinel-1 SAR images into high-resolution TerraSAR-X images, improving quality and reducing costs.

Contribution

The paper proposes a new dialectical GAN architecture combining hierarchical SAR analysis and novel loss functions for SAR image translation.

Findings

Dialectical GAN outperforms traditional algorithms in SAR image translation.

High-quality SAR images can be generated from low-resolution inputs.

The method effectively captures hierarchical SAR information.

Abstract

Contrary to optical images, Synthetic Aperture Radar (SAR) images are in different electromagnetic spectrum where the human visual system is not accustomed to. Thus, with more and more SAR applications, the demand for enhanced high-quality SAR images has increased considerably. However, high-quality SAR images entail high costs due to the limitations of current SAR devices and their image processing resources. To improve the quality of SAR images and to reduce the costs of their generation, we propose a Dialectical Generative Adversarial Network (Dialectical GAN) to generate high-quality SAR images. This method is based on the analysis of hierarchical SAR information and the "dialectical" structure of GAN frameworks. As a demonstration, a typical example will be shown where a low-resolution SAR image (e.g., a Sentinel-1 image) with large ground coverage is translated into a high-resolution SAR image (e.g., a TerraSAR-X image). Three traditional algorithms are compared, and a new algorithm is proposed based on a network framework by combining conditional WGAN-GP (Wasserstein Generative Adversarial Network - Gradient Penalty) loss functions and Spatial Gram matrices under the rule of dialectics. Experimental results show that the SAR image translation works very well when we compare the results of our proposed method with the selected traditional methods.