A multi-scale enhanced feature fusion model for aircraft detection from SAR images
Aircraft detection has been a challenging task although many efforts have been made due to the diversity of aircraft scale and interference of complicated background in synthetic aperture radar (SAR) images. So, this paper proposes a new method, named ‘multi-scale enhanced feature fusion network, br...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2507842 |
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| author | Guoqing Zhou Ziqi Zhang Feng Wang Qiang Zhu YueFeng Wang Ertao Gao Yufu Cai Xiao Zhou Cong Li |
| author_facet | Guoqing Zhou Ziqi Zhang Feng Wang Qiang Zhu YueFeng Wang Ertao Gao Yufu Cai Xiao Zhou Cong Li |
| author_sort | Guoqing Zhou |
| collection | DOAJ |
| description | Aircraft detection has been a challenging task although many efforts have been made due to the diversity of aircraft scale and interference of complicated background in synthetic aperture radar (SAR) images. So, this paper proposes a new method, named ‘multi-scale enhanced feature fusion network, briefly, MSEFF-Net’. Firstly, a nonlinear activation free attention module (NAFAM) is proposed to enhance the feature information of aircraft. Secondly, a feature fusion module (FFM) is designed and a multi-scale feature fusion pyramid network (MFFPN) is proposed to integrate the semantic information of different layers. Finally, a global-to-local context aggregation (GLCA) module is built to aggregate global and local information. The proposed model is validated using two groups of public datasets, SAR-AIRcraft-1.0 and SADD, and is compared with various advanced detection methods (e.g. Faster R-CNN, Cascade-RCNN, YOLO series and RT-DETR series). The experimental results demonstrate that the precision, the recall, and the mAP50 reach 97.4%, 97.6%, 98.9% for SADD dataset; the mAP50 and the mAP50:95 reach 70.1% and 49.0% for SAR-AIRcraft-1.0 dataset, respectively. The results indicate that the proposed method achieves higher accuracy than the other detection methods do. |
| format | Article |
| id | doaj-art-fe2443b7a51e4cb595e9a6fa96901d3f |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-fe2443b7a51e4cb595e9a6fa96901d3f2025-08-25T11:32:04ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2507842A multi-scale enhanced feature fusion model for aircraft detection from SAR imagesGuoqing Zhou0Ziqi Zhang1Feng Wang2Qiang Zhu3YueFeng Wang4Ertao Gao5Yufu Cai6Xiao Zhou7Cong Li8Guangxi Key Laboratory of Spatial Information and Geomatics, College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, People’s Republic of ChinaGuangxi Key Laboratory of Spatial Information and Geomatics, College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, People’s Republic of ChinaGuangxi Key Laboratory of Spatial Information and Geomatics, College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, People’s Republic of ChinaGuangxi Key Laboratory of Spatial Information and Geomatics, College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, People’s Republic of ChinaGuangxi Key Laboratory of Spatial Information and Geomatics, College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, People’s Republic of ChinaGuangxi Key Laboratory of Spatial Information and Geomatics, College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, People’s Republic of ChinaGuangxi Key Laboratory of Spatial Information and Geomatics, College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, People’s Republic of ChinaGuangxi Key Laboratory of Spatial Information and Geomatics, College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, People’s Republic of ChinaGuangxi Key Laboratory of Spatial Information and Geomatics, College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, People’s Republic of ChinaAircraft detection has been a challenging task although many efforts have been made due to the diversity of aircraft scale and interference of complicated background in synthetic aperture radar (SAR) images. So, this paper proposes a new method, named ‘multi-scale enhanced feature fusion network, briefly, MSEFF-Net’. Firstly, a nonlinear activation free attention module (NAFAM) is proposed to enhance the feature information of aircraft. Secondly, a feature fusion module (FFM) is designed and a multi-scale feature fusion pyramid network (MFFPN) is proposed to integrate the semantic information of different layers. Finally, a global-to-local context aggregation (GLCA) module is built to aggregate global and local information. The proposed model is validated using two groups of public datasets, SAR-AIRcraft-1.0 and SADD, and is compared with various advanced detection methods (e.g. Faster R-CNN, Cascade-RCNN, YOLO series and RT-DETR series). The experimental results demonstrate that the precision, the recall, and the mAP50 reach 97.4%, 97.6%, 98.9% for SADD dataset; the mAP50 and the mAP50:95 reach 70.1% and 49.0% for SAR-AIRcraft-1.0 dataset, respectively. The results indicate that the proposed method achieves higher accuracy than the other detection methods do.https://www.tandfonline.com/doi/10.1080/17538947.2025.2507842Synthetic aperture radar (SAR)aircraft detectionattention mechanismmulti-scale feature fusionYOLOv8 |
| spellingShingle | Guoqing Zhou Ziqi Zhang Feng Wang Qiang Zhu YueFeng Wang Ertao Gao Yufu Cai Xiao Zhou Cong Li A multi-scale enhanced feature fusion model for aircraft detection from SAR images International Journal of Digital Earth Synthetic aperture radar (SAR) aircraft detection attention mechanism multi-scale feature fusion YOLOv8 |
| title | A multi-scale enhanced feature fusion model for aircraft detection from SAR images |
| title_full | A multi-scale enhanced feature fusion model for aircraft detection from SAR images |
| title_fullStr | A multi-scale enhanced feature fusion model for aircraft detection from SAR images |
| title_full_unstemmed | A multi-scale enhanced feature fusion model for aircraft detection from SAR images |
| title_short | A multi-scale enhanced feature fusion model for aircraft detection from SAR images |
| title_sort | multi scale enhanced feature fusion model for aircraft detection from sar images |
| topic | Synthetic aperture radar (SAR) aircraft detection attention mechanism multi-scale feature fusion YOLOv8 |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2507842 |
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