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Contrary to optical images, Synthetic Aperture Radar (SAR) images are indifferent electromagnetic spectrum where the human visual system is notaccustomed to. Thus, with more and more SAR applications, the demand forenhanced high-quality SAR images has increased considerably. However,high-quality SAR images entail high costs due to the limitations of current SARdevices and their image processing resources. To improve the quality of SARimages and to reduce the costs of their generation, we propose a DialecticalGenerative Adversarial Network (Dialectical GAN) to generate high-quality SARimages. This method is based on the analysis of hierarchical SAR informationand the "dialectical" structure of GAN frameworks. As a demonstration, atypical example will be shown where a low-resolution SAR image (e.g., aSentinel-1 image) with large ground coverage is translated into ahigh-resolution SAR image (e.g., a TerraSAR-X image). Three traditionalalgorithms are compared, and a new algorithm is proposed based on a networkframework by combining conditional WGAN-GP (Wasserstein Generative AdversarialNetwork - Gradient Penalty) loss functions and Spatial Gram matrices under therule of dialectics. Experimental results show that the SAR image translationworks very well when we compare the results of our proposed method with theselected traditional methods.

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