MF-ShipNet: a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing images
Due to the complex sea background and the different sizes and shapes of ships, the detection of ship targets has the problems of low detection rate, high false detection rate and high missed detection rate. To solve this problem, this paper proposes a multi-feature weighted fusion and PCA-SVM model...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2521826 |
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| _version_ | 1849424902105333760 |
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| author | Jianfeng Li Yibing Yang Liutong Yang Yang Zhao Qinghua Luo Chenxu Wang |
| author_facet | Jianfeng Li Yibing Yang Liutong Yang Yang Zhao Qinghua Luo Chenxu Wang |
| author_sort | Jianfeng Li |
| collection | DOAJ |
| description | Due to the complex sea background and the different sizes and shapes of ships, the detection of ship targets has the problems of low detection rate, high false detection rate and high missed detection rate. To solve this problem, this paper proposes a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing images. A weighted fusion method of Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) was used to extract shape and texture features simultaneously. Principal Component Analysis (PCA) was used to reduce the dimension of fused features to reduce the false detection rate caused by redundant noise interference. The feature pyramid was constructed to realize the feature fusion of different levels. In addition, a re-screening method based on color features or geometric features is proposed to further reduce the false detection rate. The accuracy and recall rate of the proposed detection algorithm in the DFH-MODIS dataset reach 97.9%and 79.8% respectively. Compared with the ship target detection algorithm based on HOG feature combined with SVM classifier, the F1-score is improved from 0.554 to 0.879. It effectively improves the detection performance of the ship target detection algorithm in optical remote sensing images, and has better effectiveness and robustness. |
| format | Article |
| id | doaj-art-602be51ff8d24730892a2d275ca1d6bd |
| institution | Kabale University |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-602be51ff8d24730892a2d275ca1d6bd2025-08-20T03:29:58ZengTaylor & Francis GroupGeocarto International1010-60491752-07622025-12-0140110.1080/10106049.2025.2521826MF-ShipNet: a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing imagesJianfeng Li0Yibing Yang1Liutong Yang2Yang Zhao3Qinghua Luo4Chenxu Wang5The School of Information Science and Engineering, Harbin Institute of Technology (Weihai), WeihaiThe School of Information Science and Engineering, Harbin Institute of Technology (Weihai), WeihaiThe School of Instrument Science and Engineering, Harbin Institute of Technology, HarbinThe School of Information Science and Engineering, Harbin Institute of Technology (Weihai), WeihaiThe School of Information Science and Engineering, Harbin Institute of Technology (Weihai), WeihaiThe School of Information Science and Engineering, Harbin Institute of Technology (Weihai), WeihaiDue to the complex sea background and the different sizes and shapes of ships, the detection of ship targets has the problems of low detection rate, high false detection rate and high missed detection rate. To solve this problem, this paper proposes a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing images. A weighted fusion method of Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) was used to extract shape and texture features simultaneously. Principal Component Analysis (PCA) was used to reduce the dimension of fused features to reduce the false detection rate caused by redundant noise interference. The feature pyramid was constructed to realize the feature fusion of different levels. In addition, a re-screening method based on color features or geometric features is proposed to further reduce the false detection rate. The accuracy and recall rate of the proposed detection algorithm in the DFH-MODIS dataset reach 97.9%and 79.8% respectively. Compared with the ship target detection algorithm based on HOG feature combined with SVM classifier, the F1-score is improved from 0.554 to 0.879. It effectively improves the detection performance of the ship target detection algorithm in optical remote sensing images, and has better effectiveness and robustness.https://www.tandfonline.com/doi/10.1080/10106049.2025.2521826Machine learningremote sensing imageship detectionfeature fusion |
| spellingShingle | Jianfeng Li Yibing Yang Liutong Yang Yang Zhao Qinghua Luo Chenxu Wang MF-ShipNet: a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing images Geocarto International Machine learning remote sensing image ship detection feature fusion |
| title | MF-ShipNet: a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing images |
| title_full | MF-ShipNet: a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing images |
| title_fullStr | MF-ShipNet: a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing images |
| title_full_unstemmed | MF-ShipNet: a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing images |
| title_short | MF-ShipNet: a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing images |
| title_sort | mf shipnet a multi feature weighted fusion and pca svm model for ship detection in remote sensing images |
| topic | Machine learning remote sensing image ship detection feature fusion |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2025.2521826 |
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