Quality Assessment of SAR-to-Optical Image Translation

Jiexin Zhang, Jianjiang Zhou*, Minglei Li, Huiyu Zhou, Tianzhu Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

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.

Original languageEnglish
Article number3472
Number of pages25
JournalRemote Sensing
Volume12
Issue number21
DOIs
Publication statusPublished - 22 Oct 2020
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgments: This research is supported by the National Youth Science Foundation of China under (Grant No. 61501228) and the Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing Univ. Aeronaut. Astronaut.), Ministry of Education, China.

Funding Information:
This research is supported by the National Youth Science Foundation of China under (Grant No. 61501228) and the Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing Univ. Aeronaut. Astronaut.), Ministry of Education, China.

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Generative adversarial networks (GANs)
  • Image quality assessment (IQA)
  • Image restoration
  • SAR-to-optical image translation
  • Synthetic aperture radar (SAR)

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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