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Yingbo Dong
Researcher at Chinese Academy of Sciences
Publications - 5
Citations - 482
Yingbo Dong is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Synthetic aperture radar & Clutter. The author has an hindex of 3, co-authored 5 publications receiving 177 citations.
Papers
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Journal ArticleDOI
A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds
TL;DR: Experimental results reveal that object detectors achieve higher mean average precision (mAP) on the test dataset and have high generalization performance on new SAR imagery without land-ocean segmentation, demonstrating the benefits of the dataset the authors constructed.
Journal ArticleDOI
Automatic Ship Detection Based on RetinaNet Using Multi-Resolution Gaofen-3 Imagery
TL;DR: The experimental results reveal that retinaNet not only can efficiently detect multi-scale ships but also has a high detection accuracy and compared with other object detectors, RetinaNet achieves more than a 96% mean average precision (mAP).
Journal ArticleDOI
Fine-grained ship classification based on deep residual learning for high-resolution SAR images
TL;DR: The experimental results show that, the proposed framework can achieve a 99% overall accuracy on the augmented dataset under the optimal fine-tuning strategy, 3% higher than that in other models, which demonstrates the effectiveness of the proposed approach.
Proceedings ArticleDOI
Impact Analysis of Incident Angle Factor on High-Resolution Sar Image Ship Classification Based on Deep Learning
TL;DR: The first analysis of the incident angle factor in SAR ship classification using deep learning methods allowed researchers to select appropriate data when using the deep learning method to classify ships in SAR images, and may suggest satellite parameters based on the classification results.
Patent
A training method for ship detection by using a convolutional neural network and a ship detection method thereof
TL;DR: In this paper, a training method for ship detection by using a convolutional neural network and a ship detection method thereof is presented, where the training method comprises the steps of making SAR image data including ship information into training image information of a predefined specification.