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Multitask Learning for Ship Detection From Synthetic Aperture Radar Images | |
Zhang, Xin1,2; Huo, Chunlei1,2; Xu, Nuo1,2; Jiang, Hangzhi1,2; Cao, Yong1,2; Ni, Lei3; Pan, Chunhong1,2 | |
发表期刊 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING |
ISSN | 1939-1404 |
2021 | |
卷号 | 14页码:8048-8062 |
摘要 | Ship detection from synthetic aperture radar (SAR) images is inherently subject to the special imaging mechanism of SAR. In recent years, deep-learning-based techniques for detecting objects from optical images have rapidly advanced and promoted the development of SAR image detection technology. However, the strong speckle noise in SAR images degrades low-level feature learning in shallow layers, hindering the higher level learning of semantic features for object detection. In view of the problems encountered in direct end-to-end feature learning for object detection and the close relationship between objects and auxiliary cues, a multitask learning-based object detector (MTL-Det) is proposed in this article to distinguish ships in SAR images. The proposed approach models the ship detection problem, not as a single object detection task, but as three cooperative tasks. The model involves two auxiliary subtasks that are focused on learning object-specific cues (e.g., texture and shape) for the ship detection task, which is constrained by the pseudoground truth generated by the main task. Assisted by auxiliary subtasks, the low-level features are robust to speckle noise and reliably support high-level feature learning. Compared with traditional single-task-based object detectors, more discriminative object-specific features are learned by multitask learning without the extra cost of manual labeling. The experiments conducted in this study help demonstrate the advantages of MTL-Det in improving the ship detection performance on two SAR datasets: high-resolution SAR images dataset and large-scale SAR ship detection dataset-v1.0. |
关键词 | Task analysis Feature extraction Radar polarimetry Object detection Marine vehicles Detectors Synthetic aperture radar Multitask learning synthetic aperture radar (SAR) SAR ship detection |
DOI | 10.1109/JSTARS.2021.3102989 |
关键词[WOS] | TARGET DETECTION ; SAR IMAGES ; RESOLUTION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[62071466] ; National Natural Science Foundation of China[62076242] ; National Natural Science Foundation of China[61976208] ; National Key Research and Development Program of China[2018AAA0100400] |
项目资助者 | National Natural Science Foundation of China ; National Key Research and Development Program of China |
WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS类目 | Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000690441600006 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45891 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
通讯作者 | Huo, Chunlei |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Space Engn Univ, Grad Sch, Beijing 100192, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Zhang, Xin,Huo, Chunlei,Xu, Nuo,et al. Multitask Learning for Ship Detection From Synthetic Aperture Radar Images[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2021,14:8048-8062. |
APA | Zhang, Xin.,Huo, Chunlei.,Xu, Nuo.,Jiang, Hangzhi.,Cao, Yong.,...&Pan, Chunhong.(2021).Multitask Learning for Ship Detection From Synthetic Aperture Radar Images.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,8048-8062. |
MLA | Zhang, Xin,et al."Multitask Learning for Ship Detection From Synthetic Aperture Radar Images".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):8048-8062. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
MTL-Det_JSTARs.pdf(12575KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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