Microwave SAIR Imaging Approach Based on Deep Convolutional Neural Network
文献类型：期刊论文
| 作者 | Zhang, Yilong; Ren, Yuan; Miao, Wei; Lin, Zhenhui; Gao, Hao; Shi, Shengcai |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2019-12-01 |
| 卷号 | 57期号:12页码:10376-10389 |
| 关键词 | Convolutional neural network (CNN) deep learning (DL) imaging approach microwave synthetic aperture interferometric radiometers (SAIRs) |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2019.2934154 |
| 通讯作者 | Zhang, Yilong(ylzhang@pmo.ac.cn) |
| 英文摘要 | Microwave 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. |
| WOS关键词 | RECONSTRUCTION ; PERFORMANCE |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000505701800066 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://libir.pmo.ac.cn/handle/332002/35458]  |
| 专题 | 中国科学院紫金山天文台 |
| 通讯作者 | Zhang, Yilong |
| 作者单位 | Chinese Acad Sci, Purple Mt Observ, Key Lab Radio Astron, Nanjing 210033, Peoples R China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：紫金山天文台
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