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

1
German Aerospace Center (DLR), Münchener Str. 20, 82234 Wessling, Germany
2
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1597; https://doi.org/10.3390/rs10101597
Received: 18 July 2018 / Revised: 2 October 2018 / Accepted: 4 October 2018 / Published: 8 October 2018
(This article belongs to the Special Issue Deep Learning for Remote Sensing)
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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. View Full-Text
Keywords: dialectical generative adversarial network; image translation; Sentinel-1; TerraSAR-X dialectical generative adversarial network; image translation; Sentinel-1; TerraSAR-X
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Ao, D.; Dumitru, C.O.; Schwarz, G.; Datcu, M. Dialectical GAN for SAR Image Translation: From Sentinel-1 to TerraSAR-X. Remote Sens. 2018, 10, 1597.

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