CNN-Based Target Detection and Classification When Sparse SAR Image Dataset is Available
Synthetic aperture radar (SAR) is an earth observation technology that can obtain high-resolution image in all-weather and all-time conditions, and hence, has been widely used in civil and military applications. SAR target detection and classification are the key processes for the detailed feature i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9468926/ |
| _version_ | 1819155153224204288 |
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| author | Hui Bi Jiarui Deng Tianwen Yang Jian Wang Ling Wang |
| author_facet | Hui Bi Jiarui Deng Tianwen Yang Jian Wang Ling Wang |
| author_sort | Hui Bi |
| collection | DOAJ |
| description | Synthetic aperture radar (SAR) is an earth observation technology that can obtain high-resolution image in all-weather and all-time conditions, and hence, has been widely used in civil and military applications. SAR target detection and classification are the key processes for the detailed feature information extraction of the interested target. Compared with traditional matched filtering (MF) recovered result, sparse SAR image has lower sidelobes, noise, and clutter. Thus, it will theoretically has better performance in target detection and classification. In this article, we propose a novel sparse SAR image based target detection and classification framework. This novel framework first obtains the sparse SAR image dataset by complex approximate message passing (CAMP), which is an <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula>-norm regularization sparse imaging method. Different from other regularization recovery algorithms, CAMP can output not only a sparse solution, but also a nonsparse estimation of considered scene that well preserves the statistical characteristic of the image when protruding the target. Then, we detect and classify the targets by using the convolutional neural network based technologies from the sparse SAR image datasets constructed by the sparse and nonsparse solutions of CAMP, respectively. For clarify, these two kinds of sparse SAR image datasets are named as <inline-formula><tex-math notation="LaTeX">$\mathcal {D}_{\rm Sp}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathcal {D}_{\rm Nsp}$</tex-math></inline-formula>. Experimental results show that under standard operating conditions, the proposed framework can obtain 92.60% and 99.29% mAP on Faster RCNN and YOLOv3 by using the <inline-formula><tex-math notation="LaTeX">$\mathcal {D}_{\rm Nsp}$</tex-math></inline-formula> sparse SAR image dataset. Under extended operating conditions, the mAP value of Faster RCNN and YOLOv3 are 95.69% and 89.91% mAP, respectively. These values based on the <inline-formula><tex-math notation="LaTeX">$\mathcal {D}_{\rm Nsp}$</tex-math></inline-formula> dataset are much higher than the classified result based on the corresponding MF dataset. |
| first_indexed | 2024-12-22T15:32:27Z |
| format | Article |
| id | doaj.art-3846f937f8754171833d521f00566f1c |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-22T15:32:27Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-3846f937f8754171833d521f00566f1c2022-12-21T18:21:20ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01146815682610.1109/JSTARS.2021.30936459468926CNN-Based Target Detection and Classification When Sparse SAR Image Dataset is AvailableHui Bi0https://orcid.org/0000-0002-9357-8412Jiarui Deng1Tianwen Yang2Jian Wang3Ling Wang4Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Eduction, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaKey Laboratory of Radar Imaging and Microwave Photonics, Ministry of Eduction, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaNational Mobile Communications Research Laboratory, Southeast University, Nanjing, ChinaKey Laboratory of Radar Imaging and Microwave Photonics, Ministry of Eduction, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaKey Laboratory of Radar Imaging and Microwave Photonics, Ministry of Eduction, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaSynthetic aperture radar (SAR) is an earth observation technology that can obtain high-resolution image in all-weather and all-time conditions, and hence, has been widely used in civil and military applications. SAR target detection and classification are the key processes for the detailed feature information extraction of the interested target. Compared with traditional matched filtering (MF) recovered result, sparse SAR image has lower sidelobes, noise, and clutter. Thus, it will theoretically has better performance in target detection and classification. In this article, we propose a novel sparse SAR image based target detection and classification framework. This novel framework first obtains the sparse SAR image dataset by complex approximate message passing (CAMP), which is an <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula>-norm regularization sparse imaging method. Different from other regularization recovery algorithms, CAMP can output not only a sparse solution, but also a nonsparse estimation of considered scene that well preserves the statistical characteristic of the image when protruding the target. Then, we detect and classify the targets by using the convolutional neural network based technologies from the sparse SAR image datasets constructed by the sparse and nonsparse solutions of CAMP, respectively. For clarify, these two kinds of sparse SAR image datasets are named as <inline-formula><tex-math notation="LaTeX">$\mathcal {D}_{\rm Sp}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathcal {D}_{\rm Nsp}$</tex-math></inline-formula>. Experimental results show that under standard operating conditions, the proposed framework can obtain 92.60% and 99.29% mAP on Faster RCNN and YOLOv3 by using the <inline-formula><tex-math notation="LaTeX">$\mathcal {D}_{\rm Nsp}$</tex-math></inline-formula> sparse SAR image dataset. Under extended operating conditions, the mAP value of Faster RCNN and YOLOv3 are 95.69% and 89.91% mAP, respectively. These values based on the <inline-formula><tex-math notation="LaTeX">$\mathcal {D}_{\rm Nsp}$</tex-math></inline-formula> dataset are much higher than the classified result based on the corresponding MF dataset.https://ieeexplore.ieee.org/document/9468926/Convolutional neural network (CNN)complex approximate message passing (CAMP)sparse synthetic aperture radar (SAR) imagetarget detection and classification |
| spellingShingle | Hui Bi Jiarui Deng Tianwen Yang Jian Wang Ling Wang CNN-Based Target Detection and Classification When Sparse SAR Image Dataset is Available IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Convolutional neural network (CNN) complex approximate message passing (CAMP) sparse synthetic aperture radar (SAR) image target detection and classification |
| title | CNN-Based Target Detection and Classification When Sparse SAR Image Dataset is Available |
| title_full | CNN-Based Target Detection and Classification When Sparse SAR Image Dataset is Available |
| title_fullStr | CNN-Based Target Detection and Classification When Sparse SAR Image Dataset is Available |
| title_full_unstemmed | CNN-Based Target Detection and Classification When Sparse SAR Image Dataset is Available |
| title_short | CNN-Based Target Detection and Classification When Sparse SAR Image Dataset is Available |
| title_sort | cnn based target detection and classification when sparse sar image dataset is available |
| topic | Convolutional neural network (CNN) complex approximate message passing (CAMP) sparse synthetic aperture radar (SAR) image target detection and classification |
| url | https://ieeexplore.ieee.org/document/9468926/ |
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