A Neural Network Method of Selective Endmember for Pixel Unmixing

Liu Ping-xiang
{"title":"A Neural Network Method of Selective Endmember for Pixel Unmixing","authors":"Liu Ping-xiang","doi":"10.11834/jrs.20070103","DOIUrl":null,"url":null,"abstract":"Remote sensing images contain a lot of mixed image pixels,but it is difficult to classify these pixels.If the number of pixel's endmember is regarded as unchangeable,the traditional pixel unmixing algorithm cannot get a good result.In this paper we develop a new method of selective endmembers for pixel unmixing based on the fuzzy ARTMAP neural network,which firstly compares the pixel's spectral to the conference one and then gets the number of endmember.When it is taken into account,we use an ARTMAP neural network to extract subpixel information.Finally,the experimental results show that the selective endmember algorithm has been improved over conventional ANN algorithms and conventional linear algorithms.","PeriodicalId":217329,"journal":{"name":"National Remote Sensing Bulletin","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"National Remote Sensing Bulletin","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11834/jrs.20070103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Remote sensing images contain a lot of mixed image pixels,but it is difficult to classify these pixels.If the number of pixel's endmember is regarded as unchangeable,the traditional pixel unmixing algorithm cannot get a good result.In this paper we develop a new method of selective endmembers for pixel unmixing based on the fuzzy ARTMAP neural network,which firstly compares the pixel's spectral to the conference one and then gets the number of endmember.When it is taken into account,we use an ARTMAP neural network to extract subpixel information.Finally,the experimental results show that the selective endmember algorithm has been improved over conventional ANN algorithms and conventional linear algorithms.
一种用于像素解混的选择性端元神经网络方法
遥感图像包含了大量的混合像元,这些像元的分类比较困难。如果以像素端元个数不变为前提,传统的像素解混算法无法获得良好的解混效果。本文提出了一种基于模糊ARTMAP神经网络的像素解混的选择性端元方法,该方法首先将像素的光谱与会议光谱进行比较,然后得到端元个数。当考虑到这一点时,我们使用ARTMAP神经网络来提取亚像素信息。最后,实验结果表明,选择性端元算法比传统的人工神经网络算法和传统的线性算法得到了改进。
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