CASIA OpenIR  > 脑图谱与类脑智能实验室  > 微观重建与智能分析
Geolocation Error Estimation and Correction on Long-Term MWRI Data
Liu, Jiazheng1,2; Li, Weifu1,2; Peng, Jiangtao3; Shen, Lijun1; Han, Hua1,2; Zhang, Peng4; Yang, Lei4
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
2021-11-01
Volume59Issue:11Pages:9448-9461
Abstract

Due to the limitation of the satellite attitude measurement accuracy and the system servo control error of the payload scanning mechanism, an optimal use of Micro-Wave Radiation Imager (MWRI) observations requires high geolocation accuracy. In the operational system, the MWRI geolocation accuracy reaches 1 pixel, and there still exists room for improvement. In this article, we improve upon the coastline inflection point method (CIM) and propose to assign the accurate correspondence by employing a nonrigid point set registration method. First, the method identifies a set of latent variables to recognize outliers and then applies nonparametric geometric constraints to the correspondence as a priori distribution. Second, the maximum a posteriori (MAP) estimation is applied by the expectation-maximization (EM) algorithm to obtain correct inliers. The comparison with other methods demonstrates that the proposed method can provide more accurate estimation of geolocation bias. In addition, the pixel error and changes in spacecraft attitude with the long-term geolocation data in FY-3C MWRI before and after correction were analyzed during the period from April 1 to August 30, 2018. The results have shown that the geolocation errors are reduced from [0.50, 0.60] pixels to [0.20, 0.33] pixels in the along- and cross-track directions after the attitude correction. In addition, the reduction of the standard deviation shows that the geolocation quality of MWRI is improved.

KeywordGeology Sea surface Satellites Earth Instruments Satellite broadcasting Microwave radiometry Geolocation error estimation and correction Micro-Wave Radiation Imager (MWRI) nonrigid point set registration
DOI10.1109/TGRS.2021.3051199
WOS KeywordMICROWAVE ; REGISTRATION ; ACCURACY ; IMAGER
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program[2018YFB0504900] ; National Key Research and Development Program[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] ; Fundamental Research Funds for the Central Universities of China[2662020LXQD002]
Funding OrganizationNational Key Research and Development Program ; National Natural Science Foundation of China ; Scientific Instrument Developing Project of Chinese Academy of Sciences ; Fundamental Research Funds for the Central Universities of China
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:000711850900041
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
IS Representative Paper
Sub direction classification图像视频处理与分析
planning direction of the national heavy laboratoryAI For Science
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Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/46501
Collection脑图谱与类脑智能实验室_微观重建与智能分析
Corresponding AuthorYang, Lei
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Future Technol, Beijing 100049, Peoples R China
3.Hubei Univ, Fac Math & Stat, Hubei Key Lab Appl Math, Wuhan 430062, Peoples R China
4.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
Liu, Jiazheng,Li, Weifu,Peng, Jiangtao,et al. Geolocation Error Estimation and Correction on Long-Term MWRI Data[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2021,59(11):9448-9461.
APA Liu, Jiazheng.,Li, Weifu.,Peng, Jiangtao.,Shen, Lijun.,Han, Hua.,...&Yang, Lei.(2021).Geolocation Error Estimation and Correction on Long-Term MWRI Data.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,59(11),9448-9461.
MLA Liu, Jiazheng,et al."Geolocation Error Estimation and Correction on Long-Term MWRI Data".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 59.11(2021):9448-9461.
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