Multiscale DEM generation on basis of singular value decomposition

Caixian Zhang, Jun He, Wenguang Hou
{"title":"Multiscale DEM generation on basis of singular value decomposition","authors":"Caixian Zhang, Jun He, Wenguang Hou","doi":"10.1117/12.2537903","DOIUrl":null,"url":null,"abstract":"As the fundamental data about the terrains, DEM plays an important role in many fields. The high resolution DEM is increasingly popular. Yet, the multiscale resolution DEMs are still desired for some applications due to the fact that the low resolution DEM can reduce the memory demands with limited computational complexity. Then, how to obtain the multiscale DEMs remains an open question, which demands that the different resolution DEMs should discard the detailed information with maintaining the main information of the high resolution DEM. Moreover, the multiscale DEMs should not cost many memories. Generally, there is a contradiction. As such, this paper proposes a multiscale DEM generation method based on Singular Value Decomposition (SVD) which can establish multiscale DEMs maintaining the different details with a small quantity of memory increasement. The method is simple but effective. Lots of experiment shows its effectiveness.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Symposium on Multispectral Image Processing and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2537903","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

As the fundamental data about the terrains, DEM plays an important role in many fields. The high resolution DEM is increasingly popular. Yet, the multiscale resolution DEMs are still desired for some applications due to the fact that the low resolution DEM can reduce the memory demands with limited computational complexity. Then, how to obtain the multiscale DEMs remains an open question, which demands that the different resolution DEMs should discard the detailed information with maintaining the main information of the high resolution DEM. Moreover, the multiscale DEMs should not cost many memories. Generally, there is a contradiction. As such, this paper proposes a multiscale DEM generation method based on Singular Value Decomposition (SVD) which can establish multiscale DEMs maintaining the different details with a small quantity of memory increasement. The method is simple but effective. Lots of experiment shows its effectiveness.
基于奇异值分解的多尺度DEM生成
对地形的基本数据,民主党在许多领域扮演着重要的角色。高分辨率DEM越来越受欢迎。然而,由于低分辨率DEM可以在有限的计算复杂度下减少内存需求,因此在某些应用中仍然需要多尺度分辨率DEM。那么,如何获得多尺度DEM仍然是一个有待解决的问题,这就要求不同分辨率DEM在保留高分辨率DEM的主要信息的同时,放弃细节信息。此外,多尺度dem不需要占用太多内存。一般来说,这是一个矛盾。为此,本文提出了一种基于奇异值分解(SVD)的多尺度DEM生成方法,该方法可以在少量内存增量的情况下建立保持不同细节的多尺度DEM。这个方法简单而有效。大量实验证明了该方法的有效性。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信