Performance of unsupervised machine learning methods using chi-squared weights for LiDAR point cloud filtering in urban areas

IF 1 4区 地球科学 Q4 GEOGRAPHY, PHYSICAL Journal of Spatial Science Pub Date : 2021-12-27 DOI:10.1080/14498596.2021.2013329
A. Sen, B. Suleymanoglu, M. Soycan
{"title":"Performance of unsupervised machine learning methods using chi-squared weights for LiDAR point cloud filtering in urban areas","authors":"A. Sen, B. Suleymanoglu, M. Soycan","doi":"10.1080/14498596.2021.2013329","DOIUrl":null,"url":null,"abstract":"ABSTRACT In this study, we compared the LiDAR filtering performances of unsupervised machine learning methods, such as linkage, K-means, and self-organizing maps, for urban areas to provide a practical guide to researchers. The input parameters (x-y-z and intensity) were normalized and weighted using a chi-squared independence test to improve the classification accuracy. The best successful results were obtained using the weighted linkage method in terms of the total error of 13.53%, 3.96%, and 1.07% for the three samples, respectively. In comparison with other approaches, methods weighted by chi-squared have significant potential for classification and filtering and outperform many popular approaches.","PeriodicalId":50045,"journal":{"name":"Journal of Spatial Science","volume":"68 1","pages":"397 - 414"},"PeriodicalIF":1.0000,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Spatial Science","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/14498596.2021.2013329","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: 0

Abstract

ABSTRACT In this study, we compared the LiDAR filtering performances of unsupervised machine learning methods, such as linkage, K-means, and self-organizing maps, for urban areas to provide a practical guide to researchers. The input parameters (x-y-z and intensity) were normalized and weighted using a chi-squared independence test to improve the classification accuracy. The best successful results were obtained using the weighted linkage method in terms of the total error of 13.53%, 3.96%, and 1.07% for the three samples, respectively. In comparison with other approaches, methods weighted by chi-squared have significant potential for classification and filtering and outperform many popular approaches.
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
使用卡方权重的无监督机器学习方法在城市地区的LiDAR点云滤波性能
在本研究中,我们比较了无监督机器学习方法(如链接、K-means和自组织地图)在城市地区的LiDAR滤波性能,为研究人员提供实用指导。输入参数(x-y-z和强度)使用卡方独立性检验进行归一化和加权,以提高分类精度。采用加权联动法对3个样本的总误差分别为13.53%、3.96%和1.07%,获得了最佳的成功结果。与其他方法相比,卡方加权的方法具有显著的分类和过滤潜力,并且优于许多流行的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Spatial Science
Journal of Spatial Science 地学-地质学
CiteScore
5.00
自引率
5.30%
发文量
25
审稿时长
>12 weeks
期刊介绍: The Journal of Spatial Science publishes papers broadly across the spatial sciences including such areas as cartography, geodesy, geographic information science, hydrography, digital image analysis and photogrammetry, remote sensing, surveying and related areas. Two types of papers are published by he journal: Research Papers and Professional Papers. Research Papers (including reviews) are peer-reviewed and must meet a minimum standard of making a contribution to the knowledge base of an area of the spatial sciences. This can be achieved through the empirical or theoretical contribution to knowledge that produces significant new outcomes. It is anticipated that Professional Papers will be written by industry practitioners. Professional Papers describe innovative aspects of professional practise and applications that advance the development of the spatial industry.
期刊最新文献
Analysis of vegetation influence on building shadow extraction in remote sensing imagery using deep convolutional neural networks A novel approach to coral species classification using deep learning and unsupervised feature extraction Land cover classification in high-resolution remote sensing: using Swin Transformer deep learning with texture features A change detection algorithm for the SAR images based on DWT and DE optimization Predicting land use and land cover change dynamics in the eThekwini Municipality: a machine learning approach with Landsat imagery
×
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