基于稀疏度的高光谱图像泛锐化新算法

C. Kwan, Bence Budavari, Minh Dao, Jin Zhou
{"title":"基于稀疏度的高光谱图像泛锐化新算法","authors":"C. Kwan, Bence Budavari, Minh Dao, Jin Zhou","doi":"10.1109/UEMCON.2017.8248993","DOIUrl":null,"url":null,"abstract":"In this paper, we present new sparsity based algorithms in generating a high resolution hyperspectral image by fusing a high resolution color image with a low resolution hyperspectral image. Mathematical formulation of the sparsity based approaches is presented. Comparison with other pansharpening algorithms using actual data has been carried out using two hyperspectral image data sets. Initial results are encouraging. Most importantly, the new sparsity formulation points to a new direction in generating high resolution hyperspectral images where the raw images may be noisy.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"2008 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"New sparsity based pansharpening algorithms for hyperspectral images\",\"authors\":\"C. Kwan, Bence Budavari, Minh Dao, Jin Zhou\",\"doi\":\"10.1109/UEMCON.2017.8248993\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present new sparsity based algorithms in generating a high resolution hyperspectral image by fusing a high resolution color image with a low resolution hyperspectral image. Mathematical formulation of the sparsity based approaches is presented. Comparison with other pansharpening algorithms using actual data has been carried out using two hyperspectral image data sets. Initial results are encouraging. Most importantly, the new sparsity formulation points to a new direction in generating high resolution hyperspectral images where the raw images may be noisy.\",\"PeriodicalId\":403890,\"journal\":{\"name\":\"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)\",\"volume\":\"2008 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UEMCON.2017.8248993\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UEMCON.2017.8248993","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21

摘要

在本文中,我们提出了一种新的基于稀疏度的算法,通过融合高分辨率彩色图像和低分辨率高光谱图像来生成高分辨率高光谱图像。给出了基于稀疏度方法的数学表达式。利用两组高光谱图像数据,与其他泛锐化算法进行了对比。初步结果令人鼓舞。最重要的是,新的稀疏性公式指出了生成高分辨率高光谱图像的新方向,其中原始图像可能有噪声。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New sparsity based pansharpening algorithms for hyperspectral images
In this paper, we present new sparsity based algorithms in generating a high resolution hyperspectral image by fusing a high resolution color image with a low resolution hyperspectral image. Mathematical formulation of the sparsity based approaches is presented. Comparison with other pansharpening algorithms using actual data has been carried out using two hyperspectral image data sets. Initial results are encouraging. Most importantly, the new sparsity formulation points to a new direction in generating high resolution hyperspectral images where the raw images may be noisy.
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