Dual-Modal Approach for Ship Detection: Fusing Synthetic Aperture Radar and Optical Satellite Imagery
The fusion of synthetic aperture radar (SAR) and optical satellite imagery poses significant challenges for ship detection due to the distinct characteristics and noise profiles of each modality. Optical imagery provides high-resolution information but struggles in adverse weather and low-light cond...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/329 |
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| author | Mahmoud Ahmed Naser El-Sheimy Henry Leung |
| author_facet | Mahmoud Ahmed Naser El-Sheimy Henry Leung |
| author_sort | Mahmoud Ahmed |
| collection | DOAJ |
| description | The fusion of synthetic aperture radar (SAR) and optical satellite imagery poses significant challenges for ship detection due to the distinct characteristics and noise profiles of each modality. Optical imagery provides high-resolution information but struggles in adverse weather and low-light conditions, reducing its reliability for maritime applications. In contrast, SAR imagery excels in these scenarios but is prone to noise and clutter, complicating vessel detection. Existing research on SAR and optical image fusion often fails to effectively leverage their complementary strengths, resulting in suboptimal detection outcomes. This research presents a novel fusion framework designed to enhance ship detection by integrating SAR and optical imagery. This framework incorporates a detection system for optical images that utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) in combination with the YOLOv7 model to improve accuracy and processing speed. For SAR images, a customized Detection Transformer model, SAR-EDT, integrates advanced denoising algorithms and optimized pooling configurations. A fusion module evaluates the overlaps of detected bounding boxes based on intersection over union (IoU) metrics. Fused detections are generated by averaging confidence scores and recalculating bounding box dimensions, followed by robust postprocessing to eliminate duplicates. The proposed framework significantly improves ship detection accuracy across various scenarios. |
| format | Article |
| id | doaj-art-4292dc4de4a744939f22b9f896ccca3e |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-4292dc4de4a744939f22b9f896ccca3e2025-01-24T13:48:32ZengMDPI AGSensors1424-82202025-01-0125232910.3390/s25020329Dual-Modal Approach for Ship Detection: Fusing Synthetic Aperture Radar and Optical Satellite ImageryMahmoud Ahmed0Naser El-Sheimy1Henry Leung2Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaThe fusion of synthetic aperture radar (SAR) and optical satellite imagery poses significant challenges for ship detection due to the distinct characteristics and noise profiles of each modality. Optical imagery provides high-resolution information but struggles in adverse weather and low-light conditions, reducing its reliability for maritime applications. In contrast, SAR imagery excels in these scenarios but is prone to noise and clutter, complicating vessel detection. Existing research on SAR and optical image fusion often fails to effectively leverage their complementary strengths, resulting in suboptimal detection outcomes. This research presents a novel fusion framework designed to enhance ship detection by integrating SAR and optical imagery. This framework incorporates a detection system for optical images that utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) in combination with the YOLOv7 model to improve accuracy and processing speed. For SAR images, a customized Detection Transformer model, SAR-EDT, integrates advanced denoising algorithms and optimized pooling configurations. A fusion module evaluates the overlaps of detected bounding boxes based on intersection over union (IoU) metrics. Fused detections are generated by averaging confidence scores and recalculating bounding box dimensions, followed by robust postprocessing to eliminate duplicates. The proposed framework significantly improves ship detection accuracy across various scenarios.https://www.mdpi.com/1424-8220/25/2/329synthetic aperture radar (SAR)multi-modal fusiondetection transformer |
| spellingShingle | Mahmoud Ahmed Naser El-Sheimy Henry Leung Dual-Modal Approach for Ship Detection: Fusing Synthetic Aperture Radar and Optical Satellite Imagery Sensors synthetic aperture radar (SAR) multi-modal fusion detection transformer |
| title | Dual-Modal Approach for Ship Detection: Fusing Synthetic Aperture Radar and Optical Satellite Imagery |
| title_full | Dual-Modal Approach for Ship Detection: Fusing Synthetic Aperture Radar and Optical Satellite Imagery |
| title_fullStr | Dual-Modal Approach for Ship Detection: Fusing Synthetic Aperture Radar and Optical Satellite Imagery |
| title_full_unstemmed | Dual-Modal Approach for Ship Detection: Fusing Synthetic Aperture Radar and Optical Satellite Imagery |
| title_short | Dual-Modal Approach for Ship Detection: Fusing Synthetic Aperture Radar and Optical Satellite Imagery |
| title_sort | dual modal approach for ship detection fusing synthetic aperture radar and optical satellite imagery |
| topic | synthetic aperture radar (SAR) multi-modal fusion detection transformer |
| url | https://www.mdpi.com/1424-8220/25/2/329 |
| work_keys_str_mv | AT mahmoudahmed dualmodalapproachforshipdetectionfusingsyntheticapertureradarandopticalsatelliteimagery AT naserelsheimy dualmodalapproachforshipdetectionfusingsyntheticapertureradarandopticalsatelliteimagery AT henryleung dualmodalapproachforshipdetectionfusingsyntheticapertureradarandopticalsatelliteimagery |