Local structure learning in high resolution remote sensing image retrieval | |
Du, Zhongxiang1,2; Li, Xuelong1; Lu, Xiaoqiang1; Li, XL | |
作者部门 | 光学影像学习与分析中心 |
2016-09-26 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
卷号 | 207页码:813-822 |
产权排序 | 1 |
摘要 | High resolution remote sensing image captured by the satellites or the aircraft is of great help for military and civilian applications. In recent years, with an increasing amount of high-resolution remote sensing images, it becomes more and more urgent to find a way to retrieve them. In this case, a few methods based on the statistical-information of the local features are proposed, which have achieved good performances. However, most of the methods do not take the topological structure of the features into account. In this paper, we propose a new method to represent these images, by taking the structural information into consideration. The main contributions of this paper include: (1) mapping the features into a manifold space by a Lipschitz smooth function to enhance the representation ability of the features; (2) training an anchor set with several regularization constrains to get the intrinsic manifold structure. In the experiments, the method is applied to two challenging remote sensing image datasets: UC Merced land use dataset and Sydney dataset. Compared to the state-of-the-art approaches, the proposed method can achieve a more robust and commendable performance. (C) 2016 Elsevier B.V. All rights reserved. |
文章类型 | Article |
关键词 | High Resolution Remote Sensing Image Retrieval Lipschitz Smooth Manifold Structure |
学科领域 | Computer Science, Artificial Intelligence |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1016/j.neucom.2016.05.061 |
收录类别 | SCI ; EI |
关键词[WOS] | FEATURES |
语种 | 英语 |
WOS研究方向 | Computer Science |
项目资助者 | National Basic Research Program of China (973 Program)(2012CB719905) ; National Natural Science Foundation of China(61472413) ; Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences(LSIT201408) |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000382794500077 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/28412 |
专题 | 光谱成像技术研究室 |
通讯作者 | Li, XL |
作者单位 | 1.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China 2.Univ Chinese Acad Sci, 19A Yuquanlu, Beijing 100049, Peoples R China |
推荐引用方式 GB/T 7714 | Du, Zhongxiang,Li, Xuelong,Lu, Xiaoqiang,et al. Local structure learning in high resolution remote sensing image retrieval[J]. NEUROCOMPUTING,2016,207:813-822. |
APA | Du, Zhongxiang,Li, Xuelong,Lu, Xiaoqiang,&Li, XL.(2016).Local structure learning in high resolution remote sensing image retrieval.NEUROCOMPUTING,207,813-822. |
MLA | Du, Zhongxiang,et al."Local structure learning in high resolution remote sensing image retrieval".NEUROCOMPUTING 207(2016):813-822. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Local structure lear(3260KB) | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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