PMO OpenIR
Microwave SAIR Imaging Approach Based on Deep Convolutional Neural Network
Zhang, Yilong; Ren, Yuan; Miao, Wei; Lin, Zhenhui; Gao, Hao; Shi, Shengcai
2019-12-01
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
Volume57Issue:12Pages:10376-10389
Corresponding AuthorZhang, Yilong(ylzhang@pmo.ac.cn)
AbstractMicrowave synthetic aperture interferometric radiometers (SAIRs) are very powerful instruments for high-resolution remote sensing of the atmosphere and the earth surfaces at microwave frequencies. Microwave SAIR imaging reconstruction from interferometric measurements suffers from hardware non-identities, limited prior information, and noise interference, and consequently often requires expert calibration strategies to reduce imaging error and improve the accuracy of the reconstruction. In this article, we propose a new SAIR imaging approach with a deep convolutional neural network (CNN) learning framework to optimize the reconstruction performance. We interpret interferometric measurements of SAIR as a signal encoding representation and SAIR imaging as the corresponding decoding representation. A deep CNN framework with additional fully connected layers is utilized to autonomously learn the decoding representation from interferometric measurement samples and perform SAIR imaging. The supervised learning forward model with hyperparameters makes that the proposed approach could accurately obtain the SAIR imaging representation involving multiple systematic features for real applications. We demonstrate the performance of the proposed imaging approach through extensive numerical experiments. Compared with conventional handcrafted Fourier transform and sparse regularization reconstruction imaging approaches, the proposed imaging approach based on deep learning is superior in terms of image quality, computing efficiency, and noise suppression.
KeywordConvolutional neural network (CNN) deep learning (DL) imaging approach microwave synthetic aperture interferometric radiometers (SAIRs)
DOI10.1109/TGRS.2019.2934154
WOS KeywordRECONSTRUCTION ; PERFORMANCE
Indexed BySCI
Language英语
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000505701800066
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://libir.pmo.ac.cn/handle/332002/35459
Collection中国科学院紫金山天文台
Corresponding AuthorZhang, Yilong
AffiliationChinese Acad Sci, Purple Mt Observ, Key Lab Radio Astron, Nanjing 210033, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Yilong,Ren, Yuan,Miao, Wei,et al. Microwave SAIR Imaging Approach Based on Deep Convolutional Neural Network[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2019,57(12):10376-10389.
APA Zhang, Yilong,Ren, Yuan,Miao, Wei,Lin, Zhenhui,Gao, Hao,&Shi, Shengcai.(2019).Microwave SAIR Imaging Approach Based on Deep Convolutional Neural Network.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,57(12),10376-10389.
MLA Zhang, Yilong,et al."Microwave SAIR Imaging Approach Based on Deep Convolutional Neural Network".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 57.12(2019):10376-10389.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhang, Yilong]'s Articles
[Ren, Yuan]'s Articles
[Miao, Wei]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhang, Yilong]'s Articles
[Ren, Yuan]'s Articles
[Miao, Wei]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhang, Yilong]'s Articles
[Ren, Yuan]'s Articles
[Miao, Wei]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.