Deep Metric Learning for Fine-Grained Ship Classification in SAR Images with Sidelobe Interference
The interference of sidelobe often causes different targets to exhibit similar features, diminishing fine-grained classification accuracy. This effect is particularly pronounced when the available data are limited. To address the aforementioned issues, a novel classification framework for sidelobe-a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/11/1835 |
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| author | Haibin Zhu Yaxin Mu Wupeng Xie Kang Xing Bin Tan Yashi Zhou Zhongde Yu Zhiying Cui Chuang Zhang Xin Liu Zhenghuan Xia |
| author_facet | Haibin Zhu Yaxin Mu Wupeng Xie Kang Xing Bin Tan Yashi Zhou Zhongde Yu Zhiying Cui Chuang Zhang Xin Liu Zhenghuan Xia |
| author_sort | Haibin Zhu |
| collection | DOAJ |
| description | The interference of sidelobe often causes different targets to exhibit similar features, diminishing fine-grained classification accuracy. This effect is particularly pronounced when the available data are limited. To address the aforementioned issues, a novel classification framework for sidelobe-affected SAR imagery is proposed. First, a method based on maximum median filtering is adopted to remove sidelobe by exploiting local grayscale differences between the target and sidelobe, constructing a high-quality SAR dataset. Second, a deep metric learning network is constructed for fine-grained classification. To enhance the classification performance of the network on limited samples, a feature extraction module integrating a lightweight attention mechanism is designed to extract discriminative features. Then, a hybrid loss function is proposed to strengthen intra-class correlation and inter-class separability. Experimental results based on the FUSAR-Ship dataset demonstrate that the method exhibits excellent sidelobe suppression performance. Furthermore, the proposed framework achieves an accuracy of 84.18% across five ship target classification categories, outperforming the existing methods, significantly enhancing the classification performance in the context of sidelobe interference and limited datasets. |
| format | Article |
| id | doaj-art-db206d2d30f7480eaf4a0f24972a7442 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-db206d2d30f7480eaf4a0f24972a74422025-08-20T02:23:44ZengMDPI AGRemote Sensing2072-42922025-05-011711183510.3390/rs17111835Deep Metric Learning for Fine-Grained Ship Classification in SAR Images with Sidelobe InterferenceHaibin Zhu0Yaxin Mu1Wupeng Xie2Kang Xing3Bin Tan4Yashi Zhou5Zhongde Yu6Zhiying Cui7Chuang Zhang8Xin Liu9Zhenghuan Xia10School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, ChinaSchool of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, ChinaArtificial Intelligence Institute of China Electronics Technology Group Corporation, Beijing 100041, ChinaBeijing Institute of Satellite Information Engineering, Beijing 100095, ChinaBeijing Institute of Satellite Information Engineering, Beijing 100095, ChinaChina Academy of Space Technology, Beijing 100094, ChinaBeijing Institute of Satellite Information Engineering, Beijing 100095, ChinaBeijing Institute of Satellite Information Engineering, Beijing 100095, ChinaBeijing Institute of Satellite Information Engineering, Beijing 100095, ChinaBeijing Institute of Satellite Information Engineering, Beijing 100095, ChinaBeijing Institute of Satellite Information Engineering, Beijing 100095, ChinaThe interference of sidelobe often causes different targets to exhibit similar features, diminishing fine-grained classification accuracy. This effect is particularly pronounced when the available data are limited. To address the aforementioned issues, a novel classification framework for sidelobe-affected SAR imagery is proposed. First, a method based on maximum median filtering is adopted to remove sidelobe by exploiting local grayscale differences between the target and sidelobe, constructing a high-quality SAR dataset. Second, a deep metric learning network is constructed for fine-grained classification. To enhance the classification performance of the network on limited samples, a feature extraction module integrating a lightweight attention mechanism is designed to extract discriminative features. Then, a hybrid loss function is proposed to strengthen intra-class correlation and inter-class separability. Experimental results based on the FUSAR-Ship dataset demonstrate that the method exhibits excellent sidelobe suppression performance. Furthermore, the proposed framework achieves an accuracy of 84.18% across five ship target classification categories, outperforming the existing methods, significantly enhancing the classification performance in the context of sidelobe interference and limited datasets.https://www.mdpi.com/2072-4292/17/11/1835synthetic aperture radarfine-grained ship classificationsidelobe effectmaximum median filterdeep metric learning |
| spellingShingle | Haibin Zhu Yaxin Mu Wupeng Xie Kang Xing Bin Tan Yashi Zhou Zhongde Yu Zhiying Cui Chuang Zhang Xin Liu Zhenghuan Xia Deep Metric Learning for Fine-Grained Ship Classification in SAR Images with Sidelobe Interference Remote Sensing synthetic aperture radar fine-grained ship classification sidelobe effect maximum median filter deep metric learning |
| title | Deep Metric Learning for Fine-Grained Ship Classification in SAR Images with Sidelobe Interference |
| title_full | Deep Metric Learning for Fine-Grained Ship Classification in SAR Images with Sidelobe Interference |
| title_fullStr | Deep Metric Learning for Fine-Grained Ship Classification in SAR Images with Sidelobe Interference |
| title_full_unstemmed | Deep Metric Learning for Fine-Grained Ship Classification in SAR Images with Sidelobe Interference |
| title_short | Deep Metric Learning for Fine-Grained Ship Classification in SAR Images with Sidelobe Interference |
| title_sort | deep metric learning for fine grained ship classification in sar images with sidelobe interference |
| topic | synthetic aperture radar fine-grained ship classification sidelobe effect maximum median filter deep metric learning |
| url | https://www.mdpi.com/2072-4292/17/11/1835 |
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