Abstract
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). 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.
Highlights
In remote sensing, Synthetic Aperture Radar (SAR) images are well-known for their all-time and all-weather capabilities
In order to evaluate the image quality of the SAR images, we introduce the equivalent numbers of looks (ENL), which act as a contrast factor to represent the image resolutions approximately
In order to evaluate the image quality of the SAR images, we introduce the equivalent numbers of looks (ENL), which act as a contrast factor to represent the image rweseoilnuttrioondsuaceppthroexeiqmuaivteallye.nAt lnthuomubgehrsthoefEloNoLksis(EanNiLn)d, iwcahtiocrhfoarctspasecakcleo,nittrhaasts faancteograttoivreecporrerseelnattitohne wimitahgtehreeismolaugteiornessoalpuptiroonx.imHaetreel,y.thAe lEthNoLugish ctohme EpuNtLedisinanonintdhiecawtohrofloerpsaptecchk. lLe,ooitkhsaasrea tnheegasutibv-e icmoarrgeelsatifoonrmweidthdthueriinmgagSAe rResporluoctieosnsi.nHgerwe,htehree EtNheL iims caogme psupteecdklien ovnartihanecwe hioslerepdautcched
Summary
Synthetic Aperture Radar (SAR) images are well-known for their all-time and all-weather capabilities. We demonstrate a typical example where a low-resolution SAR image (e.g., a Sentinel-1 image) with large ground coverage is translated using deep learning into a high-resolution SAR image (e.g., a TerraSAR-X image). To some extent, this kind of translation is related to super-resolution and neural style transfer. It is much easier for researchers to access open-source Sentinel-1 images than the charged TerraSAR-X images To meet these requirements for high-quality data, we propose a “Dialectical GAN” (DiGAN) method based on the analysis of the hierarchical SAR information and the “dialectical” structure of GAN frameworks.
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