{"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}
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.
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.