Visible to the public SAR Image Super-Resolution Based on Noise-Free Generative Adversarial Network

TitleSAR Image Super-Resolution Based on Noise-Free Generative Adversarial Network
Publication TypeConference Paper
Year of Publication2019
AuthorsGu, Feng, Zhang, Hong, Wang, Chao, Wu, Fan
Conference NameIGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Date Publishedjul
Keywordsadversarial objective function, deep generative adversarial network, Deep Learning, DGAN, Generative Adversarial Learning, generative adversarial network, generative adversarial networks, Generators, image denoising, Image reconstruction, Image resolution, learning (artificial intelligence), Metrics, neural nets, noise free generative adversarial network, pseudo high-resolution SAR images, pubcrawl, radar computing, radar imaging, Radar polarimetry, radar resolution, realistic HR images, resilience, Resiliency, SAR image superresolution, Scalability, Super-resolution, synthetic aperture radar, synthetic aperture radar images, Training

Deep learning has been successfully applied to the ordinary image super-resolution (SR). However, since the synthetic aperture radar (SAR) images are often disturbed by multiplicative noise known as speckle and more blurry than ordinary images, there are few deep learning methods for the SAR image SR. In this paper, a deep generative adversarial network (DGAN) is proposed to reconstruct the pseudo high-resolution (HR) SAR images. First, a generator network is constructed to remove the noise of low-resolution SAR image and generate HR SAR image. Second, a discriminator network is used to differentiate between the pseudo super-resolution images and the realistic HR images. The adversarial objective function is introduced to make the pseudo HR SAR images closer to real SAR images. The experimental results show that our method can maintain the SAR image content with high-level noise suppression. The performance evaluation based on peak signal-to-noise-ratio and structural similarity index shows the superiority of the proposed method to the conventional CNN baselines.

Citation Keygu_sar_2019