多光谱图像多尺度区域分类的层次马尔可夫模型

A. Katartzis, I. Vanhamel, H. Sahli
{"title":"多光谱图像多尺度区域分类的层次马尔可夫模型","authors":"A. Katartzis, I. Vanhamel, H. Sahli","doi":"10.1109/WARSD.2003.1295223","DOIUrl":null,"url":null,"abstract":"We propose a new multispectral image classification method, based on a Markovian model, defined on the hierarchy of a multiscale region adjacency graph. The paper describes the main principles of our method and illustrates classification results on a set of artificial and remote sensing images, together with qualitative and quantitative comparisons with a variety of multi-and single-resolution Bayesian classification approaches.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A hierarchical Markovian model for multiscale region-based classification of multispectral images\",\"authors\":\"A. Katartzis, I. Vanhamel, H. Sahli\",\"doi\":\"10.1109/WARSD.2003.1295223\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a new multispectral image classification method, based on a Markovian model, defined on the hierarchy of a multiscale region adjacency graph. The paper describes the main principles of our method and illustrates classification results on a set of artificial and remote sensing images, together with qualitative and quantitative comparisons with a variety of multi-and single-resolution Bayesian classification approaches.\",\"PeriodicalId\":395735,\"journal\":{\"name\":\"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-10-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WARSD.2003.1295223\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WARSD.2003.1295223","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

提出了一种基于马尔可夫模型的多光谱图像分类方法,该方法基于多尺度区域邻接图的层次结构。本文介绍了该方法的主要原理,并举例说明了一组人工和遥感图像的分类结果,并与各种多分辨率和单分辨率贝叶斯分类方法进行了定性和定量比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hierarchical Markovian model for multiscale region-based classification of multispectral images
We propose a new multispectral image classification method, based on a Markovian model, defined on the hierarchy of a multiscale region adjacency graph. The paper describes the main principles of our method and illustrates classification results on a set of artificial and remote sensing images, together with qualitative and quantitative comparisons with a variety of multi-and single-resolution Bayesian classification approaches.
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