使用距离评估方法进行几何质量控制的有效性

IF 1.8 Q3 REMOTE SENSING International Journal of Image and Data Fusion Pub Date : 2019-07-15 DOI:10.1080/19479832.2019.1641164

J. Zelasco, J. Donayo

{"title":"使用距离评估方法进行几何质量控制的有效性","authors":"J. Zelasco, J. Donayo","doi":"10.1080/19479832.2019.1641164","DOIUrl":null,"url":null,"abstract":"ABSTRACT Research studies have shown that when the error of a digital elevation model (DEM) is accepted as random, one of the two solutions is generally used. One is the employment of a benchmark, and the other is measuring the vertical distance to evaluate the vertical error. For the first solution, a benchmark or adequate random sample may be unavailable. The second solution consists of measuring only the vertical distance from a point of the DEM to the surface of the reference DEM. This solution provides only a biased vertical error. In this paper, a perpendicular distance evaluation method (PDEM) is proposed. This approach allows estimating the vertical error, and under certain irregular terrain conditions, the horizontal error. Simulations are presented in detail, starting from a real reference DEM, with 100 000 points. The results confirm that, the more irregular the terrain is, the better the horizontal error results, and that the evaluation of the vertical error is not biased.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"10 1","pages":"263 - 279"},"PeriodicalIF":1.8000,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2019.1641164","citationCount":"1","resultStr":"{\"title\":\"Effectiveness of geometric quality control using a distance evaluation method\",\"authors\":\"J. Zelasco, J. Donayo\",\"doi\":\"10.1080/19479832.2019.1641164\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Research studies have shown that when the error of a digital elevation model (DEM) is accepted as random, one of the two solutions is generally used. One is the employment of a benchmark, and the other is measuring the vertical distance to evaluate the vertical error. For the first solution, a benchmark or adequate random sample may be unavailable. The second solution consists of measuring only the vertical distance from a point of the DEM to the surface of the reference DEM. This solution provides only a biased vertical error. In this paper, a perpendicular distance evaluation method (PDEM) is proposed. This approach allows estimating the vertical error, and under certain irregular terrain conditions, the horizontal error. Simulations are presented in detail, starting from a real reference DEM, with 100 000 points. The results confirm that, the more irregular the terrain is, the better the horizontal error results, and that the evaluation of the vertical error is not biased.\",\"PeriodicalId\":46012,\"journal\":{\"name\":\"International Journal of Image and Data Fusion\",\"volume\":\"10 1\",\"pages\":\"263 - 279\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2019-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/19479832.2019.1641164\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Image and Data Fusion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/19479832.2019.1641164\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Image and Data Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/19479832.2019.1641164","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"REMOTE SENSING","Score":null,"Total":0}

引用次数: 1

摘要

研究表明，当接受数字高程模型(DEM)误差为随机时，通常采用两种解决方案中的一种。一种是使用基准，另一种是测量垂直距离来评估垂直误差。对于第一个解决方案，可能无法获得基准测试或足够的随机样本。第二种解决方案包括仅测量从DEM的一个点到参考DEM表面的垂直距离。这种解决方案只提供有偏的垂直误差。提出了一种垂直距离评价方法(PDEM)。这种方法可以估计垂直误差，在某些不规则地形条件下，可以估计水平误差。从一个真实的参考DEM开始，详细地进行了模拟，有10万个点。结果证实，地形越不规则，水平误差结果越好，垂直误差的评价没有偏差。

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

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Effectiveness of geometric quality control using a distance evaluation method

ABSTRACT Research studies have shown that when the error of a digital elevation model (DEM) is accepted as random, one of the two solutions is generally used. One is the employment of a benchmark, and the other is measuring the vertical distance to evaluate the vertical error. For the first solution, a benchmark or adequate random sample may be unavailable. The second solution consists of measuring only the vertical distance from a point of the DEM to the surface of the reference DEM. This solution provides only a biased vertical error. In this paper, a perpendicular distance evaluation method (PDEM) is proposed. This approach allows estimating the vertical error, and under certain irregular terrain conditions, the horizontal error. Simulations are presented in detail, starting from a real reference DEM, with 100 000 points. The results confirm that, the more irregular the terrain is, the better the horizontal error results, and that the evaluation of the vertical error is not biased.

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

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

期刊介绍： International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).