Some Unmixing Problems and Algorithms in Spectroscopy and Hyperspectral Imaging

M. Berman
{"title":"Some Unmixing Problems and Algorithms in Spectroscopy and Hyperspectral Imaging","authors":"M. Berman","doi":"10.1109/AIPR.2006.37","DOIUrl":null,"url":null,"abstract":"The automated identification and mapping of the constituent materials in a hyperspectral image is a problem of considerable interest. A significant issue is that the spectra at many pixels in such an image are actually mixtures of the spectra of the pure constituents. I review methods of \"unmixing\" spectra into their pure constituents, both when a \"spectral library\" of the pure constituents is available, and where no such library is available. Our own algorithms in both these areas are exemplified with a mineral and a biological example.","PeriodicalId":375571,"journal":{"name":"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIPR.2006.37","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

The automated identification and mapping of the constituent materials in a hyperspectral image is a problem of considerable interest. A significant issue is that the spectra at many pixels in such an image are actually mixtures of the spectra of the pure constituents. I review methods of "unmixing" spectra into their pure constituents, both when a "spectral library" of the pure constituents is available, and where no such library is available. Our own algorithms in both these areas are exemplified with a mineral and a biological example.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
光谱学和高光谱成像中的若干解混问题及算法
高光谱图像中组成物质的自动识别和映射是一个相当有趣的问题。一个重要的问题是,在这样的图像中,许多像素的光谱实际上是纯成分光谱的混合物。我回顾了将光谱“分解”成纯成分的方法,当纯成分的“光谱库”可用时,以及没有这样的库可用时。我们在这两个领域的算法都以矿物和生物为例。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Evaluation of Algorithms for Tracking Multiple Objects in Video Rapid Automated Polygonal Image Decomposition Application Development Framework for the Rapid Integration of High Performance Image Processing Algorithms Automatic Alignment of Color Imagery onto 3D Laser Radar Data A Rate Distortion Method for Beamforming in RF Image Formation
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1