Hyperparameter Configuration Learning for Ship Detection From Synthetic Aperture Radar Images
文献类型：期刊论文
| 作者 | Xu, Nuo1,2 ; Huo, Chunlei1,2; Zhang, Xin1,2; Cao, Yong1,2; Pan, Chunhong1,2
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| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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| 出版日期 | 2022 |
| 卷号 | 19页码:5 |
| 关键词 | Radar polarimetry Synthetic aperture radar Marine vehicles Training Feature extraction Optimization Optical sensors Hyperparameter configuration learning (HCL) object detection reinforcement learning (RL) synthetic aperture radar (SAR) |
| ISSN号 | 1545-598X |
| DOI | 10.1109/LGRS.2021.3139098 |
| 通讯作者 | Huo, Chunlei(clhuo@nlpr.ia.ac.cn) |
| 英文摘要 | Detecting ships from synthetic aperture radar (SAR) images is inherently subject to its imaging mechanism. With the development of deep learning, advanced learning-based techniques have been migrated from optical images to SAR images. However, the default hyperparameters (e.g., learning rate, size of the anchor box) predefined by a heuristic strategy on optical images might be suboptimal for SAR datasets. In addition, the low-quality imaging in SAR images further reduces the portability of hyperparameters. To solve this problem, a new optimization method, named reinforcement learning and hyperband (RLH), is proposed to dynamically learn hyperparameter configurations by deep reinforcement learning (DRL), where a neural network is adopted to capture the relationship between different configurations and predict new configurations to further improve the performance. Hyperparameter configuration is able to be automatically learned to accommodate various SAR image datasets, and experiments on two SAR image datasets demonstrate the effectiveness and advantage of the proposed approach. |
| 资助项目 | National Key Research and Development Program of China[2018AAA0100400] ; Natural Science Foundation of China[62071466] ; Natural Science Foundation of China[91438105] ; Natural Science Foundation of China[62076242] ; Natural Science Foundation of China[61976208] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000742729100003 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/47054]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
| 通讯作者 | Huo, Chunlei |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Nuo,Huo, Chunlei,Zhang, Xin,et al. Hyperparameter Configuration Learning for Ship Detection From Synthetic Aperture Radar Images[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2022,19:5. |
| APA | Xu, Nuo,Huo, Chunlei,Zhang, Xin,Cao, Yong,&Pan, Chunhong.(2022).Hyperparameter Configuration Learning for Ship Detection From Synthetic Aperture Radar Images.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19,5. |
| MLA | Xu, Nuo,et al."Hyperparameter Configuration Learning for Ship Detection From Synthetic Aperture Radar Images".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022):5. |
入库方式： OAI收割
来源：自动化研究所
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