Synthetic Aperture Radar Learning-imaging Method Based on Data-driven Technique and Artificial Intelligence
One of the most important research fields in Synthetic Aperture Radar (SAR) technology is to improve the accuracies of the number, location, classification, and other parameters of targets of interest. SAR information processing can be mainly divided into two tasks: imaging and interpretation. At pr...
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| Format: | Article |
| Language: | English |
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China Science Publishing & Media Ltd. (CSPM)
2020-02-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | http://radars.ie.ac.cn/article/doi/10.12000/JR19103?viewType=HTML |
| _version_ | 1797428109611368448 |
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| author | LUO Ying NI Jiacheng ZHANG Qun |
| author_facet | LUO Ying NI Jiacheng ZHANG Qun |
| author_sort | LUO Ying |
| collection | DOAJ |
| description | One of the most important research fields in Synthetic Aperture Radar (SAR) technology is to improve the accuracies of the number, location, classification, and other parameters of targets of interest. SAR information processing can be mainly divided into two tasks: imaging and interpretation. At present, research efforts on these two tasks are relatively independent. Many algorithms have been developed for SAR imaging and interpretation, and they have become increasingly complex. However, SAR interpretation has not been made simpler by improvements in the imaging resolution. The low recognition rate of key targets, in particular, has yet to be adequately resolved. In this paper, we use a “data driven + intelligence learning” method to improve the information processing ability of airborne SAR based on SAR imaging & interpretation integration. First, we analyze the feasibility and main problems of SAR imaging & interpretation integration using a “data driven + intelligence learning” method. Based on the results, we propose a SAR learning-imaging method based on “data driven + intelligence learning” with the goal of producing an imaging network. The proposed learning-imaging framework, parameter selection method, network training method, and preliminary simulation results are presented, and the key technical problems to be solved are identified and analyzed. |
| first_indexed | 2024-03-09T08:54:26Z |
| format | Article |
| id | doaj.art-a6f96e767ed147a7be244eb98ecd059e |
| institution | Directory Open Access Journal |
| issn | 2095-283X 2095-283X |
| language | English |
| last_indexed | 2024-03-09T08:54:26Z |
| publishDate | 2020-02-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj.art-a6f96e767ed147a7be244eb98ecd059e2023-12-02T13:39:31ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2095-283X2020-02-019110712210.12000/JR19103Synthetic Aperture Radar Learning-imaging Method Based on Data-driven Technique and Artificial IntelligenceLUO Ying0NI Jiacheng1ZHANG Qun2①(College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China)②(Collaborative Innovation Center of Information Sensing and Understanding, Xi’an 710077, China)③(The Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), Fudan University, Shanghai 200433, China)①(College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China)②(Collaborative Innovation Center of Information Sensing and Understanding, Xi’an 710077, China)①(College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China)②(Collaborative Innovation Center of Information Sensing and Understanding, Xi’an 710077, China)③(The Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), Fudan University, Shanghai 200433, China)One of the most important research fields in Synthetic Aperture Radar (SAR) technology is to improve the accuracies of the number, location, classification, and other parameters of targets of interest. SAR information processing can be mainly divided into two tasks: imaging and interpretation. At present, research efforts on these two tasks are relatively independent. Many algorithms have been developed for SAR imaging and interpretation, and they have become increasingly complex. However, SAR interpretation has not been made simpler by improvements in the imaging resolution. The low recognition rate of key targets, in particular, has yet to be adequately resolved. In this paper, we use a “data driven + intelligence learning” method to improve the information processing ability of airborne SAR based on SAR imaging & interpretation integration. First, we analyze the feasibility and main problems of SAR imaging & interpretation integration using a “data driven + intelligence learning” method. Based on the results, we propose a SAR learning-imaging method based on “data driven + intelligence learning” with the goal of producing an imaging network. The proposed learning-imaging framework, parameter selection method, network training method, and preliminary simulation results are presented, and the key technical problems to be solved are identified and analyzed.http://radars.ie.ac.cn/article/doi/10.12000/JR19103?viewType=HTMLsynthetic aperture radar (sar)sar imaging & interpretation integrationsar learningimagingdata drivendeep learning |
| spellingShingle | LUO Ying NI Jiacheng ZHANG Qun Synthetic Aperture Radar Learning-imaging Method Based on Data-driven Technique and Artificial Intelligence Leida xuebao synthetic aperture radar (sar) sar imaging & interpretation integration sar learningimaging data driven deep learning |
| title | Synthetic Aperture Radar Learning-imaging Method Based on Data-driven Technique and Artificial Intelligence |
| title_full | Synthetic Aperture Radar Learning-imaging Method Based on Data-driven Technique and Artificial Intelligence |
| title_fullStr | Synthetic Aperture Radar Learning-imaging Method Based on Data-driven Technique and Artificial Intelligence |
| title_full_unstemmed | Synthetic Aperture Radar Learning-imaging Method Based on Data-driven Technique and Artificial Intelligence |
| title_short | Synthetic Aperture Radar Learning-imaging Method Based on Data-driven Technique and Artificial Intelligence |
| title_sort | synthetic aperture radar learning imaging method based on data driven technique and artificial intelligence |
| topic | synthetic aperture radar (sar) sar imaging & interpretation integration sar learningimaging data driven deep learning |
| url | http://radars.ie.ac.cn/article/doi/10.12000/JR19103?viewType=HTML |
| work_keys_str_mv | AT luoying syntheticapertureradarlearningimagingmethodbasedondatadriventechniqueandartificialintelligence AT nijiacheng syntheticapertureradarlearningimagingmethodbasedondatadriventechniqueandartificialintelligence AT zhangqun syntheticapertureradarlearningimagingmethodbasedondatadriventechniqueandartificialintelligence |