基于相对相邻关系和局部加权距离统计的ICESat-2光子点云数据滤波方法

IF 4 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters Pub Date : 2021-11-01 DOI:10.1109/lgrs.2020.3011215

Yi Li, Haiqiang Fu, Jianjun Zhu, Changcheng Wang

{"title":"基于相对相邻关系和局部加权距离统计的ICESat-2光子点云数据滤波方法","authors":"Yi Li, Haiqiang Fu, Jianjun Zhu, Changcheng Wang","doi":"10.1109/lgrs.2020.3011215","DOIUrl":null,"url":null,"abstract":"The existing local distance statistics-based filtering method for photon point cloud data is greatly affected by the input parameter (number of photon neighbors) and has a poor ability to remove noise photons that are adjacent to signal photons. In this letter, the relative neighboring relationship (RNR) is proposed to describe the relative density distribution of the neighboring photon points around two photon points. The mean local weighted distance is then defined, which is used to enhance the discrimination between the noise photons adjacent to the signal photons and the signal photons. Finally, according to the statistical characteristics of the mean local weighted distance, two strategies for threshold selection are used to separate signal photons from noise photons. ICESat-2 data acquired over tropical forest were used to verify the performance of the proposed method, and the results showed that: 1) the proposed method has a better ability to remove the noise photons adjacent to signal photons and 2) its performance is not greatly dependent on the input parameter.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1891-1895"},"PeriodicalIF":4.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3011215","citationCount":"9","resultStr":"{\"title\":\"A Filtering Method for ICESat-2 Photon Point Cloud Data Based on Relative Neighboring Relationship and Local Weighted Distance Statistics\",\"authors\":\"Yi Li, Haiqiang Fu, Jianjun Zhu, Changcheng Wang\",\"doi\":\"10.1109/lgrs.2020.3011215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The existing local distance statistics-based filtering method for photon point cloud data is greatly affected by the input parameter (number of photon neighbors) and has a poor ability to remove noise photons that are adjacent to signal photons. In this letter, the relative neighboring relationship (RNR) is proposed to describe the relative density distribution of the neighboring photon points around two photon points. The mean local weighted distance is then defined, which is used to enhance the discrimination between the noise photons adjacent to the signal photons and the signal photons. Finally, according to the statistical characteristics of the mean local weighted distance, two strategies for threshold selection are used to separate signal photons from noise photons. ICESat-2 data acquired over tropical forest were used to verify the performance of the proposed method, and the results showed that: 1) the proposed method has a better ability to remove the noise photons adjacent to signal photons and 2) its performance is not greatly dependent on the input parameter.\",\"PeriodicalId\":13046,\"journal\":{\"name\":\"IEEE Geoscience and Remote Sensing Letters\",\"volume\":\"18 1\",\"pages\":\"1891-1895\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/lgrs.2020.3011215\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Geoscience and Remote Sensing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1109/lgrs.2020.3011215\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Geoscience and Remote Sensing Letters","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/lgrs.2020.3011215","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 9

摘要

现有的基于局部距离统计的光子点云数据滤波方法受输入参数(光子邻居数)的影响较大，对信号光子附近的噪声光子去除能力较差。本文提出了相对相邻关系(RNR)来描述两个光子点周围相邻光子点的相对密度分布。然后定义了局部加权平均距离，用于增强信号光子附近的噪声光子与信号光子的区分能力。最后，根据局部加权距离均值的统计特性，采用两种阈值选择策略对信号光子和噪声光子进行分离。利用热带森林上空的ICESat-2数据验证了该方法的性能，结果表明:1)该方法对信号光子附近的噪声光子具有较好的去除能力;2)该方法的性能对输入参数的依赖性不大。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A Filtering Method for ICESat-2 Photon Point Cloud Data Based on Relative Neighboring Relationship and Local Weighted Distance Statistics

The existing local distance statistics-based filtering method for photon point cloud data is greatly affected by the input parameter (number of photon neighbors) and has a poor ability to remove noise photons that are adjacent to signal photons. In this letter, the relative neighboring relationship (RNR) is proposed to describe the relative density distribution of the neighboring photon points around two photon points. The mean local weighted distance is then defined, which is used to enhance the discrimination between the noise photons adjacent to the signal photons and the signal photons. Finally, according to the statistical characteristics of the mean local weighted distance, two strategies for threshold selection are used to separate signal photons from noise photons. ICESat-2 data acquired over tropical forest were used to verify the performance of the proposed method, and the results showed that: 1) the proposed method has a better ability to remove the noise photons adjacent to signal photons and 2) its performance is not greatly dependent on the input parameter.

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

IEEE Geoscience and Remote Sensing Letters 工程技术-地球化学与地球物理

CiteScore

7.60

自引率

12.50%

发文量

1113

审稿时长

3.4 months

期刊介绍： IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.