一种改进的高光谱图像像素纯度指数方法

P. Bajorski, N. J. Sanders
{"title":"一种改进的高光谱图像像素纯度指数方法","authors":"P. Bajorski, N. J. Sanders","doi":"10.1109/WHISPERS.2010.5594948","DOIUrl":null,"url":null,"abstract":"This paper discusses issues with the Pixel Purity Index (PPI) method, which is a currently popular way to find endmembers in hyperspectral images. Due to randomness of PPI, it does not produce an entirely uniform set of directions. Consequently, some directions are favored in the space of pixel vectors, resulting in biased endmember identification. To overcome this difficulty, we propose a new method of construction with non-random uniform directions, which results in a more balanced identification of endmembers. Using a family of artificial examples, we show conditions under which the new method outperforms the classic PPI. In all scenarios, the new method is at least as good as the classic PPI.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A modified Pixel Purity Index method for hyperspectral images\",\"authors\":\"P. Bajorski, N. J. Sanders\",\"doi\":\"10.1109/WHISPERS.2010.5594948\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper discusses issues with the Pixel Purity Index (PPI) method, which is a currently popular way to find endmembers in hyperspectral images. Due to randomness of PPI, it does not produce an entirely uniform set of directions. Consequently, some directions are favored in the space of pixel vectors, resulting in biased endmember identification. To overcome this difficulty, we propose a new method of construction with non-random uniform directions, which results in a more balanced identification of endmembers. Using a family of artificial examples, we show conditions under which the new method outperforms the classic PPI. In all scenarios, the new method is at least as good as the classic PPI.\",\"PeriodicalId\":193944,\"journal\":{\"name\":\"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WHISPERS.2010.5594948\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WHISPERS.2010.5594948","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

本文讨论了像素纯度指数(PPI)方法的问题,这是目前在高光谱图像中寻找端元的一种流行的方法。由于PPI的随机性,它不能产生一个完全统一的方向。因此,在像素向量空间中,某些方向是有利的,从而导致有偏的端元识别。为了克服这一困难,我们提出了一种新的非随机均匀方向的构造方法,从而使端元的识别更加平衡。通过一系列人工例子,我们展示了新方法优于经典PPI的条件。在所有情况下,新方法至少与经典PPI一样好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A modified Pixel Purity Index method for hyperspectral images
This paper discusses issues with the Pixel Purity Index (PPI) method, which is a currently popular way to find endmembers in hyperspectral images. Due to randomness of PPI, it does not produce an entirely uniform set of directions. Consequently, some directions are favored in the space of pixel vectors, resulting in biased endmember identification. To overcome this difficulty, we propose a new method of construction with non-random uniform directions, which results in a more balanced identification of endmembers. Using a family of artificial examples, we show conditions under which the new method outperforms the classic PPI. In all scenarios, the new method is at least as good as the classic PPI.
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:604180095
Book学术官方微信