Automatic Clustering of Hyperspectral Data

R. Salomon, S. Dolberg, S. Rotman
{"title":"Automatic Clustering of Hyperspectral Data","authors":"R. Salomon, S. Dolberg, S. Rotman","doi":"10.1109/EEEI.2006.321094","DOIUrl":null,"url":null,"abstract":"The main goal of this research is to examine some new methods for the automatic clustering of hyperspectral data. Hyperspectral data consists of images which originate from the same physical phenomena at various wavelengths. Use of this data is common not only for medical processing but also for military purposes. In this unique research we will analyze hyperspectral data which has been taken of different types of events that evolve both temporally and spectrally. These events would seemingly be indistinguishable if only the spectral or the temporal dimensions were used. By exploiting the unique attributes of the hyperspectral temporal data, we show that we can significantly improve our target assignment capabilities. We will develop methods to evaluate our ability to correctly assign these events from each other. We will discuss how to automatically cluster such events and determine how many different types of events actually exist. Practical problems previously discussed in the literature will be demonstrated.","PeriodicalId":142814,"journal":{"name":"2006 IEEE 24th Convention of Electrical & Electronics Engineers in Israel","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE 24th Convention of Electrical & Electronics Engineers in Israel","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EEEI.2006.321094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

The main goal of this research is to examine some new methods for the automatic clustering of hyperspectral data. Hyperspectral data consists of images which originate from the same physical phenomena at various wavelengths. Use of this data is common not only for medical processing but also for military purposes. In this unique research we will analyze hyperspectral data which has been taken of different types of events that evolve both temporally and spectrally. These events would seemingly be indistinguishable if only the spectral or the temporal dimensions were used. By exploiting the unique attributes of the hyperspectral temporal data, we show that we can significantly improve our target assignment capabilities. We will develop methods to evaluate our ability to correctly assign these events from each other. We will discuss how to automatically cluster such events and determine how many different types of events actually exist. Practical problems previously discussed in the literature will be demonstrated.
高光谱数据的自动聚类
本研究的主要目的是探讨一些高光谱数据自动聚类的新方法。高光谱数据由来自不同波长的相同物理现象的图像组成。这些数据不仅普遍用于医疗处理,而且也用于军事目的。在这个独特的研究中,我们将分析高光谱数据,这些数据是由不同类型的事件在时间和光谱上演变而来的。如果只使用光谱或时间维度,这些事件似乎无法区分。通过利用高光谱时间数据的独特属性,我们可以显著提高我们的目标分配能力。我们将开发方法来评估我们正确分配这些事件的能力。我们将讨论如何自动对这些事件进行聚类,并确定实际存在多少种不同类型的事件。将演示先前在文献中讨论的实际问题。
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:604180095
Book学术官方微信