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Despeckling Representation for Data-Efficient SAR Ship Detection
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DOI：10.1109/LGRS.2024.3520955.png)
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摘要
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Deep learning techniques are extensively applied to synthetic aperture radar (SAR) ship detection tasks. Nonetheless, the limited availability of labeled SAR images impedes the neural network's ability to learn and extract robust object features from SAR ship images. To alleviate the dependence on large datasets, this letter introduces a despeckle-based representation learning approach for SAR ship detection, named despeckling ship detection YOLO (DS-YOLO). The DS-YOLO model integrates a shared feature extractor, a detection head, and a despeckling head, facilitating the concurrent performance of SAR image despeckling and ship detection. The model effectively reduces the potential for neural network overfitting by conducting joint learning of detection and despeckling processes. As a result, DS-YOLO is particularly well-suited for SAR ship detection tasks with constrained training data. Comprehensive experiments indicate that DS-YOLO substantially surpasses the performance of the conventional detection model, particularly in scenarios where labeled data are severely restricted. Source codes are available at https://github.com/Cthanta/DS-YOLO.
Keyword:
Marine vehicles
Feature extraction
YOLO
Head
Training
Radar polarimetry
Synthetic aperture radar
Speckle
Representation learning
Measurement
ship detection
synthetic aperture radar (SAR)
YOLOv5
期刊
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16.4
论文数:
1.0W
被引数:
5.1K
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YOLOShipTracker: Tracking ships in SAR images using lightweight YOLOv8YOLOShipTracker: 使用轻量级YOLOv8跟踪SAR图像中的船只
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分享收藏A polarization fusion network with geometric feature emb e dding for SAR ship classification.png)
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