评估当前实地图和激光雷达“虚拟”图,作为历史和当前陆地卫星数据的多时间分析分类程序指南,以确定森林年龄等级

W. Cooke, C. Prabhu, R. Wallis, J. Morris, B. Smith, J. Gilreath
{"title":"评估当前实地图和激光雷达“虚拟”图,作为历史和当前陆地卫星数据的多时间分析分类程序指南,以确定森林年龄等级","authors":"W. Cooke, C. Prabhu, R. Wallis, J. Morris, B. Smith, J. Gilreath","doi":"10.1109/AMTRSI.2005.1469842","DOIUrl":null,"url":null,"abstract":"Euclidean distance measures and Landsat ETM+ band combinations and transformations are tested for their usefulness in classification of volume. Field plots are used for training and validation. Early results indicate that a Euclidean distance of 18 optimizes classification accuracies for volume. Tests for optimal band combinations were inconclusive.","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Assessment of current field plots and lidar 'virtual' plots as guides to classification procedures for multitemporal analysis of historic and current landsat data for determining forest age classes\",\"authors\":\"W. Cooke, C. Prabhu, R. Wallis, J. Morris, B. Smith, J. Gilreath\",\"doi\":\"10.1109/AMTRSI.2005.1469842\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Euclidean distance measures and Landsat ETM+ band combinations and transformations are tested for their usefulness in classification of volume. Field plots are used for training and validation. Early results indicate that a Euclidean distance of 18 optimizes classification accuracies for volume. Tests for optimal band combinations were inconclusive.\",\"PeriodicalId\":302923,\"journal\":{\"name\":\"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AMTRSI.2005.1469842\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AMTRSI.2005.1469842","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

欧几里得距离测量和Landsat ETM+波段组合和转换在体积分类中的有效性进行了测试。现场图用于训练和验证。早期的结果表明,欧几里得距离为18对体积的分类精度是最佳的。最佳波段组合的试验尚无定论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of current field plots and lidar 'virtual' plots as guides to classification procedures for multitemporal analysis of historic and current landsat data for determining forest age classes
Euclidean distance measures and Landsat ETM+ band combinations and transformations are tested for their usefulness in classification of volume. Field plots are used for training and validation. Early results indicate that a Euclidean distance of 18 optimizes classification accuracies for volume. Tests for optimal band combinations were inconclusive.
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
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