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Open AccessArticle

Quality Assessment of SAR-to-Optical Image Translation

1
Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2
School of Informatics, University of Leicester, Leicester LE1 7RH, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(21), 3472; https://doi.org/10.3390/rs12213472
Received: 16 September 2020 / Revised: 16 October 2020 / Accepted: 19 October 2020 / Published: 22 October 2020
Synthetic aperture radar (SAR) images contain severe speckle noise and weak texture, which are unsuitable for visual interpretation. Many studies have been undertaken so far toward exploring the use of SAR-to-optical image translation to obtain near optical representations. However, how to evaluate the translation quality is a challenge. In this paper, we combine image quality assessment (IQA) with SAR-to-optical image translation to pursue a suitable evaluation approach. Firstly, several machine-learning baselines for SAR-to-optical image translation are established and evaluated. Then, extensive comparisons of perceptual IQA models are performed in terms of their use as objective functions for the optimization of image restoration. In order to study feature extraction of the images translated from SAR to optical modes, an application in scene classification is presented. Finally, the attributes of the translated image representations are evaluated using visual inspection and the proposed IQA methods. View Full-Text
Keywords: synthetic aperture radar (SAR); generative adversarial networks (GANs); SAR-to-optical image translation; image quality assessment (IQA); image restoration synthetic aperture radar (SAR); generative adversarial networks (GANs); SAR-to-optical image translation; image quality assessment (IQA); image restoration
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Zhang, J.; Zhou, J.; Li, M.; Zhou, H.; Yu, T. Quality Assessment of SAR-to-Optical Image Translation. Remote Sens. 2020, 12, 3472.

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