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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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2507842 |
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| Summary: | 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. |
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| ISSN: | 1753-8947 1753-8955 |