CASIA OpenIR  > 模式识别国家重点实验室  > 三维可视计算
From Point to Region: Accurate and Efficient Hierarchical Small Object Detection in Low-Resolution Remote Sensing Images
Wu, Jingqian1; Xu, Shibiao2
Source PublicationREMOTE SENSING
2021-07-01
Volume13Issue:13Pages:16
Corresponding AuthorXu, Shibiao(shibiao.xu@nlpr.ia.ac.cn)
AbstractAccurate object detection is important in computer vision. However, detecting small objects in low-resolution images remains a challenging and elusive problem, primarily because these objects are constructed of less visual information and cannot be easily distinguished from similar background regions. To resolve this problem, we propose a Hierarchical Small Object Detection Network in low-resolution remote sensing images, named HSOD-Net. We develop a point-to-region detection paradigm by first performing a key-point prediction to obtain position hypotheses, then only later super-resolving the image and detecting the objects around those candidate positions. By postponing the object prediction to after increasing its resolution, the obtained key-points are more stable than their traditional counterparts based on early object detection with less visual information. This hierarchical approach, HSOD-Net, saves significant run-time, which makes it more suitable for practical applications such as search and rescue, and drone navigation. In comparison with the state-of-art models, HSOD-Net achieves remarkable precision in detecting small objects in low-resolution remote sensing images.
Keywordsmall object detection key-point prediction image enhancement low resolution
DOI10.3390/rs13132620
Indexed BySCI
Language英语
WOS Research AreaEnvironmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000670955800001
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/45278
Collection模式识别国家重点实验室_三维可视计算
Corresponding AuthorXu, Shibiao
Affiliation1.Wake Forest Univ, Dept Comp Sci, Winston Salem, NC 27019 USA
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wu, Jingqian,Xu, Shibiao. From Point to Region: Accurate and Efficient Hierarchical Small Object Detection in Low-Resolution Remote Sensing Images[J]. REMOTE SENSING,2021,13(13):16.
APA Wu, Jingqian,&Xu, Shibiao.(2021).From Point to Region: Accurate and Efficient Hierarchical Small Object Detection in Low-Resolution Remote Sensing Images.REMOTE SENSING,13(13),16.
MLA Wu, Jingqian,et al."From Point to Region: Accurate and Efficient Hierarchical Small Object Detection in Low-Resolution Remote Sensing Images".REMOTE SENSING 13.13(2021):16.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wu, Jingqian]'s Articles
[Xu, Shibiao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wu, Jingqian]'s Articles
[Xu, Shibiao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wu, Jingqian]'s Articles
[Xu, Shibiao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.