IMHE OpenIR  > 数字山地与遥感应用中心
Integrated quality evaluation of the in-situ networking measurements and upscaling using Gaussian process regression
Language英语
Yin, Gaofei; Li, Ainong
Corresponding AuthorLi, Ainong
2017
Source PublicationISPRS Geospatial Week 2017 ; International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, v 42, n 2W7
Pages567-571
Conference Date2017-9-18~22
Conference PlaceWuhan, China
PublisherInternational Society for Photogrammetry and Remote Sensing
Funding OrganizationISPRS
Abstract

The flourished development of wireless sensor network technology sheds light to the effective and inexpensive collection of in-situ networking measurements. This will contribute to the temporal validation of coarse resolution remote sensing products. However, the quality evaluation of the in-situ networking measurements and upscaling is still problematic. This study proposed an evaluation method based on Gaussian Process Regression (GPR). Specifically, the qualities of networking measurements and upscaling were evaluated through the relevance of each plot, and the pixelwise coefficient of variation of the scaling results. Both of which can be generated by GPR. The preliminary results demonstrated the potential of the proposed method on quality evaluation of upscaling. Its potential on measurements (per se) quality evaluation will be analysed future.

KeywordIn-situ Networking Observations Upscaling Quality Evaluation Gaussian Process Regression Uncertainty
DOIAccession number: 20174204271760
ISSN1682-1750
Indexed ByEI
Accession numberAccession number: 20174204271760
Citation statistics
Document Type会议论文
Identifierhttp://ir.imde.ac.cn/handle/131551/19148
Collection数字山地与遥感应用中心
AffiliationInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu; 610041, China
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Recommended Citation
GB/T 7714
Yin, Gaofei,Li, Ainong. Integrated quality evaluation of the in-situ networking measurements and upscaling using Gaussian process regression[C]:International Society for Photogrammetry and Remote Sensing,2017:567-571.
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