文章基本信息

标题：Brightness Compensation for GF-3 SAR Images Based on Cycle-Consistent Adversarial Networks
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作者：Shaofeng Lin ; Zengguo Sun ; Xuejun Peng 等
期刊名称：European Integration Studies
印刷版ISSN：2335-8831
出版年度：2021
卷号：50
期号：4
页码：706-721
DOI：10.5755/j01.itc.50.4.29484
语种：English
出版社：Kaunas University of Technology
摘要：GF-3 is the first C-Band full-polarimetric synthetic aperture radar (SAR) satellite with a space resolution up to 1m in China. The uneven brightness of SAR images is a problem when using GF-3 images, which makes it difficult to use and produce SAR images. In this paper, a brightness compensation method is proposed for GF-3 SAR images with unbalanced brightness in some areas based on a deep learning model named Cycle-Consistent Adversarial Networks (CycleGAN). The proposed method makes the image brightness relatively consistent, and it is compared with the MASK dodging algorithm, Wallis dodging algorithm and histogram equalization in terms of the profiles, brightness mean, standard deviation, and average gradient. Results of brightness compensation show that, the proposed method makes the inner brightness differences smaller, and the image quality is obviously improved, which provides even brightness image for subsequent applications, and has great practical significance.
关键词：GF-3 synthetic aperture radar images;brightness compensation;Cycle-Consistent Adversarial Networks;deep learning