Method of joint wavelet thresholding and F-NLM de-noising for high-resolution SAR ship detection
ObjectiveAiming at the significant features of high-resolution synthetic aperture radar (SAR) ship targets with multiple scenes, multi-scale and dense arrangements, and the problem of the blurring of target edge details due to coherent noise in the imaging process, a high-resolution SAR ship detecti...
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| Format: | Article |
| Language: | English |
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Editorial Office of Chinese Journal of Ship Research
2024-12-01
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| Series: | Zhongguo Jianchuan Yanjiu |
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| Online Access: | http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03477 |
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| _version_ | 1846098388907458560 |
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| author | Liang TONG Dan LIU Zhongbo PENG Han ZOU Lumeng WANG Chunyu ZHANG |
| author_facet | Liang TONG Dan LIU Zhongbo PENG Han ZOU Lumeng WANG Chunyu ZHANG |
| author_sort | Liang TONG |
| collection | DOAJ |
| description | ObjectiveAiming at the significant features of high-resolution synthetic aperture radar (SAR) ship targets with multiple scenes, multi-scale and dense arrangements, and the problem of the blurring of target edge details due to coherent noise in the imaging process, a high-resolution SAR ship detection method is proposed with joint wavelet thresholding and fast non-local mean (F-NLM) de-noising. MethodsFirst, wavelet thresholding and F-NLM de-noising modules are utilized to preprocess the SAR image and reduce the sea clutter noise, enhance the detailed features and edge information of the detection target, and make the extracted features more discriminative. Next, a YOLOv7 detection algorithm combined with a bi-directional feature pyramid network (Bi-FPN) is selected to effectively aggregate the multi-scale features and further improve the model's accuracy. ResultsThe experimental results show that the average precision of ship detection using the de-noised dataset D-SSDD can reach 98.69% and the false alarm rate is reduced to 2.37%.ConclusionsIt is clear that the proposed high-resolution SAR ship detection method not only homogenizes the background clutter to improve the image quality, but also improves the interactivity of multi-scale feature information to ensure precise and accurate target detection. |
| format | Article |
| id | doaj-art-0bfa96c5c75c4d0782c5219b4b0c08a2 |
| institution | Kabale University |
| issn | 1673-3185 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Editorial Office of Chinese Journal of Ship Research |
| record_format | Article |
| series | Zhongguo Jianchuan Yanjiu |
| spelling | doaj-art-0bfa96c5c75c4d0782c5219b4b0c08a22025-01-02T00:51:28ZengEditorial Office of Chinese Journal of Ship ResearchZhongguo Jianchuan Yanjiu1673-31852024-12-0119627528310.19693/j.issn.1673-3185.03477ZG3477Method of joint wavelet thresholding and F-NLM de-noising for high-resolution SAR ship detectionLiang TONG0Dan LIU1Zhongbo PENG2Han ZOU3Lumeng WANG4Chunyu ZHANG5School of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing 400074, ChinaObjectiveAiming at the significant features of high-resolution synthetic aperture radar (SAR) ship targets with multiple scenes, multi-scale and dense arrangements, and the problem of the blurring of target edge details due to coherent noise in the imaging process, a high-resolution SAR ship detection method is proposed with joint wavelet thresholding and fast non-local mean (F-NLM) de-noising. MethodsFirst, wavelet thresholding and F-NLM de-noising modules are utilized to preprocess the SAR image and reduce the sea clutter noise, enhance the detailed features and edge information of the detection target, and make the extracted features more discriminative. Next, a YOLOv7 detection algorithm combined with a bi-directional feature pyramid network (Bi-FPN) is selected to effectively aggregate the multi-scale features and further improve the model's accuracy. ResultsThe experimental results show that the average precision of ship detection using the de-noised dataset D-SSDD can reach 98.69% and the false alarm rate is reduced to 2.37%.ConclusionsIt is clear that the proposed high-resolution SAR ship detection method not only homogenizes the background clutter to improve the image quality, but also improves the interactivity of multi-scale feature information to ensure precise and accurate target detection.http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03477radar target recognitionimage processingsar ship detectionwavelet transformswavelet thresholdfast non-local mean (f-nlm)bi-directional feature pyramid network (bi- fpn)yolov7 |
| spellingShingle | Liang TONG Dan LIU Zhongbo PENG Han ZOU Lumeng WANG Chunyu ZHANG Method of joint wavelet thresholding and F-NLM de-noising for high-resolution SAR ship detection Zhongguo Jianchuan Yanjiu radar target recognition image processing sar ship detection wavelet transforms wavelet threshold fast non-local mean (f-nlm) bi-directional feature pyramid network (bi- fpn) yolov7 |
| title | Method of joint wavelet thresholding and F-NLM de-noising for high-resolution SAR ship detection |
| title_full | Method of joint wavelet thresholding and F-NLM de-noising for high-resolution SAR ship detection |
| title_fullStr | Method of joint wavelet thresholding and F-NLM de-noising for high-resolution SAR ship detection |
| title_full_unstemmed | Method of joint wavelet thresholding and F-NLM de-noising for high-resolution SAR ship detection |
| title_short | Method of joint wavelet thresholding and F-NLM de-noising for high-resolution SAR ship detection |
| title_sort | method of joint wavelet thresholding and f nlm de noising for high resolution sar ship detection |
| topic | radar target recognition image processing sar ship detection wavelet transforms wavelet threshold fast non-local mean (f-nlm) bi-directional feature pyramid network (bi- fpn) yolov7 |
| url | http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03477 |
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