利用小UASs采集的图像检测地理位置偏差

IF 1 4区 地球科学 Q4 GEOGRAPHY, PHYSICAL Photogrammetric Engineering and Remote Sensing Pub Date : 2021-09-01 DOI:10.14358/pers.20-00124
J. Thayn, Aaron M. Paque, Megan C. Maher
{"title":"利用小UASs采集的图像检测地理位置偏差","authors":"J. Thayn, Aaron M. Paque, Megan C. Maher","doi":"10.14358/pers.20-00124","DOIUrl":null,"url":null,"abstract":"Statistical methods for detecting bias in global positioning system (GPS) error are presented and applied to imagery collected using three common unmanned aerial systems (UASs). Imagery processed without ground control points (GCPs)\n had horizontal errors of 1.0–2.5 m; however, the errors had unequal variances, significant directional bias, and did not conform to the expected statistical distribution and so should be considered unreliable. When GCPswere used, horizontal errors decreased\n to less than 5 cm, and the errors had equal variances, directional uniformity, and they conformed to the expected distribution. The analysis identified a longitudinal bias in some of the reference data, which were subsequently excluded from the analysis. Had these data been retained, the estimates\n of positional accuracy would have been unreliable and inaccurate. These results strongly suggest that examining GPS data for bias should be a much more common practice.","PeriodicalId":49702,"journal":{"name":"Photogrammetric Engineering and Remote Sensing","volume":"9 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Detecting Geo-Positional Bias in Imagery Collected Using Small UASs\",\"authors\":\"J. Thayn, Aaron M. Paque, Megan C. Maher\",\"doi\":\"10.14358/pers.20-00124\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Statistical methods for detecting bias in global positioning system (GPS) error are presented and applied to imagery collected using three common unmanned aerial systems (UASs). Imagery processed without ground control points (GCPs)\\n had horizontal errors of 1.0–2.5 m; however, the errors had unequal variances, significant directional bias, and did not conform to the expected statistical distribution and so should be considered unreliable. When GCPswere used, horizontal errors decreased\\n to less than 5 cm, and the errors had equal variances, directional uniformity, and they conformed to the expected distribution. The analysis identified a longitudinal bias in some of the reference data, which were subsequently excluded from the analysis. Had these data been retained, the estimates\\n of positional accuracy would have been unreliable and inaccurate. These results strongly suggest that examining GPS data for bias should be a much more common practice.\",\"PeriodicalId\":49702,\"journal\":{\"name\":\"Photogrammetric Engineering and Remote Sensing\",\"volume\":\"9 1\",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Photogrammetric Engineering and Remote Sensing\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.14358/pers.20-00124\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Photogrammetric Engineering and Remote Sensing","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.14358/pers.20-00124","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
引用次数: 1

摘要

提出了全球定位系统(GPS)误差偏差检测的统计方法,并将其应用于三种常见的无人机系统(UASs)采集的图像。在没有地面控制点(gcp)的情况下处理的图像水平误差为1.0-2.5 m;但误差方差不等,方向性偏差显著,不符合预期的统计分布,不可靠。使用gcps时,水平误差减小到5 cm以内,且误差方差相等,方向均匀,符合预期分布。分析发现一些参考数据存在纵向偏差,这些数据随后被排除在分析之外。如果保留这些数据,对位置精度的估计将是不可靠和不准确的。这些结果强烈表明,检查GPS数据的偏差应该是一种更普遍的做法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Detecting Geo-Positional Bias in Imagery Collected Using Small UASs
Statistical methods for detecting bias in global positioning system (GPS) error are presented and applied to imagery collected using three common unmanned aerial systems (UASs). Imagery processed without ground control points (GCPs) had horizontal errors of 1.0–2.5 m; however, the errors had unequal variances, significant directional bias, and did not conform to the expected statistical distribution and so should be considered unreliable. When GCPswere used, horizontal errors decreased to less than 5 cm, and the errors had equal variances, directional uniformity, and they conformed to the expected distribution. The analysis identified a longitudinal bias in some of the reference data, which were subsequently excluded from the analysis. Had these data been retained, the estimates of positional accuracy would have been unreliable and inaccurate. These results strongly suggest that examining GPS data for bias should be a much more common practice.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Photogrammetric Engineering and Remote Sensing
Photogrammetric Engineering and Remote Sensing 地学-成像科学与照相技术
CiteScore
1.70
自引率
15.40%
发文量
89
审稿时长
9 months
期刊介绍: Photogrammetric Engineering & Remote Sensing commonly referred to as PE&RS, is the official journal of imaging and geospatial information science and technology. Included in the journal on a regular basis are highlight articles such as the popular columns “Grids & Datums” and “Mapping Matters” and peer reviewed technical papers. We publish thousands of documents, reports, codes, and informational articles in and about the industries relating to Geospatial Sciences, Remote Sensing, Photogrammetry and other imaging sciences.
期刊最新文献
A Powerful Correspondence Selection Method for Point Cloud Registration Based on Machine Learning Identification of Critical Urban Clusters for Placating Urban Heat Island Effects over Fast-Growing Tropical City Regions: Estimating the Contribution of Different City Sizes in Escalating UHI Intensity A Novel Object Detection Method for Solid Waste Incorporating a Weighted Deformable Convolution GIS Tips & Tricks ‐ Relationships Count when Mapping? An Integrated Approach for Wildfire Photography Telemetry using WRF Numerical Forecast Products
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1