Brightness Compensation for GF-3 SAR Images Based on Cycle-Consistent Adversarial Networks
Keywords:GF-3 synthetic aperture radar images, brightness compensation, Cycle-Consistent Adversarial Networks, deep learning
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.
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