CASIA OpenIR  > 类脑智能研究中心  > 微观重建与智能分析
A New Geolocation Error Estimation Method in MWRI Data Aboard FY3 Series Satellites
Li, Weifu1,2; Zhao, Xinghui1,2; Peng, Jiangtao1,2; Luo, Zhicheng1,2; Shen, Lijun2; Han, Hua2; Zhang, Peng3; Yang, Lei3
Source PublicationIEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN1545-598X
2020-02-01
Volume17Issue:2Pages:197-201
Corresponding AuthorYang, Lei(yangl@cma.gov.cn)
AbstractKnown as input in the numerical weather prediction (NWP) models, microwave radiation imager (MWRI) data have been widely distributed to the user community. Nevertheless, the current operational geolocation accuracy is still on the pixel scale due to the presence of geolocation uncertainty. In this letter, we propose a new method to estimate the geolocation errors in MWRI data. Compared to the traditional coastline inflection method (CIM), the proposed method has two innovations. First, we establish a surface fitting interpolation model by involving more observations to detect the coastline. Second, we employ the iterative closest point (ICP) algorithm to determine the correspondences between the detected coastline and the actual coastline. Simulated experimental results demonstrate that the proposed method can provide a more accurate geolocation error estimation than the CIM. By applying our method, we have processed an MWRI data set from January 1 to February 28 in 2016. The experimental results have shown that the operational FY-3C MWRI geolocation errors are 0.4813 and 0.4909 pixels in the along-track and cross-track directions, respectively, which can be significantly reduced to 0.1299 and 0.1497 pixels after the attitude correction. It means that the geolocation accuracy has an average improvement up to 70%.
KeywordCoastline detection geolocation error measurement iterative closest point (ICP) microwave radiation imager (MWRI)
DOI10.1109/LGRS.2019.2920660
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Problem[2018YFB0504900] ; National Key Research and Development Problem[2018YFB0504905] ; National Natural Science Foundation of China[11771130] ; National Natural Science Foundation of China[61871177] ; National Natural Science Foundation of China[61673381] ; National Natural Science Foundation of China[61701497] ; National Natural Science Foundation of China[91338109] ; National Natural Science Foundation of China[61172113] ; Scientific Instrument Developing Project of Chinese Academy of Sciences[YZ201671] ; National Key Research and Development Problem[2018YFB0504900] ; National Key Research and Development Problem[2018YFB0504905] ; National Natural Science Foundation of China[11771130] ; National Natural Science Foundation of China[61871177] ; National Natural Science Foundation of China[61673381] ; National Natural Science Foundation of China[61701497] ; National Natural Science Foundation of China[91338109] ; National Natural Science Foundation of China[61172113] ; Scientific Instrument Developing Project of Chinese Academy of Sciences[YZ201671]
Funding OrganizationNational Key Research and Development Problem ; National Natural Science Foundation of China ; Scientific Instrument Developing Project of Chinese Academy of Sciences
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000510900300003
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28561
Collection类脑智能研究中心_微观重建与智能分析
Corresponding AuthorYang, Lei
Affiliation1.Hubei Univ, Fac Math & Stat, Wuhan 430062, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.China Meteorol Adm, Natl Satellite Meteorol Ctr, Beijing 100081, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Weifu,Zhao, Xinghui,Peng, Jiangtao,et al. A New Geolocation Error Estimation Method in MWRI Data Aboard FY3 Series Satellites[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2020,17(2):197-201.
APA Li, Weifu.,Zhao, Xinghui.,Peng, Jiangtao.,Luo, Zhicheng.,Shen, Lijun.,...&Yang, Lei.(2020).A New Geolocation Error Estimation Method in MWRI Data Aboard FY3 Series Satellites.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,17(2),197-201.
MLA Li, Weifu,et al."A New Geolocation Error Estimation Method in MWRI Data Aboard FY3 Series Satellites".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 17.2(2020):197-201.
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
[Li, Weifu]'s Articles
[Zhao, Xinghui]'s Articles
[Peng, Jiangtao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Weifu]'s Articles
[Zhao, Xinghui]'s Articles
[Peng, Jiangtao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Weifu]'s Articles
[Zhao, Xinghui]'s Articles
[Peng, Jiangtao]'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.