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SAR-to-Optical Image Translation With Hierarchical Latent Features

文献类型：期刊论文

作者Wang, Haixia3,5; Zhang, Zhigang3,5; Hu, Zhanyi1,2,4; Dong, Qiulei1,2,4
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2022
卷号60页码:12
ISSN号0196-2892
关键词Optical imaging Optical sensors Image reconstruction Radar polarimetry Adaptive optics Optical design Synthetic aperture radar Parallel generative adversarial model SAR synthetic aperture radar (SAR)-to-optical image translation
DOI10.1109/TGRS.2022.3200996
通讯作者Dong, Qiulei(qldong@nlpr.ia.ac.cn)
英文摘要Due to the all-weather and all-time imaging capability of synthetic aperture radar (SAR), SAR remote sensing analysis has attracted much attention recently. However, compared with the optical images, SAR images are more difficult to be interpreted. If an SAR image could be translated into its corresponding optical image, then the generated optical image would be helpful for assisting the interpretation. Addressing this issue, we investigate how to translate SAR images into optical ones in this work and propose a parallel generative adversarial model for SAR-to-optical image translation, called parallel generative adversarial network (Parallel-GAN), consisting of a backbone image translation subnetwork and an adjoint optical image reconstruction subnetwork. Under the proposed model, the backbone image translation subnetwork is designed to translate SAR images into optical ones, and simultaneously some of its intermediate layers are required to output similar latent features to those from the corresponding layers of the adjoint image reconstruction subnetwork. Thanks to the imposed hierarchical latent optical features, the proposed Parallel-GAN could achieve the SAR-to-optical image translation effectively. Extensive experimental results on three public datasets demonstrate that the proposed method outperforms ten state-of-the-art methods for SAR-to-optical image translation.
WOS关键词NETWORK
资助项目National Natural Science Foundation of China[61991423] ; National Natural Science Foundation of China[U1805264] ; National Natural Science Foundation of China[62073199] ; National Natural Science Foundation of China[61773245] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32050100] ; Natural Science Foundation of Shandong Province[ZR2020MF095] ; Science and Technology Project of Qingdao West Coast New Area[2021-6] ; Beijing Municipal Science and Technology Project[Z211100011021004]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000852237000008
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Natural Science Foundation of Shandong Province ; Science and Technology Project of Qingdao West Coast New Area ; Beijing Municipal Science and Technology Project
源URL[http://ir.ia.ac.cn/handle/173211/50077]  
专题自动化研究所_模式识别国家重点实验室_机器人视觉团队
通讯作者Dong, Qiulei
作者单位1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Shandong Univ Sci & Technol, Key Lab Robot & Intelligent Technol Shandong Prov, Qingdao 266590, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
5.Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Haixia,Zhang, Zhigang,Hu, Zhanyi,et al. SAR-to-Optical Image Translation With Hierarchical Latent Features[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:12.
APA Wang, Haixia,Zhang, Zhigang,Hu, Zhanyi,&Dong, Qiulei.(2022).SAR-to-Optical Image Translation With Hierarchical Latent Features.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,12.
MLA Wang, Haixia,et al."SAR-to-Optical Image Translation With Hierarchical Latent Features".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):12.

入库方式： OAI收割

来源：自动化研究所

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