Spatial-spectral graph regularized sparse nonnegative matrix factorization hyperspectral unmixing

Lin Lei, Hao Zhang, Shaoquan Zhang, Chengzhi Deng, Fan Li, Shengqian Wang
{"title":"Spatial-spectral graph regularized sparse nonnegative matrix factorization hyperspectral unmixing","authors":"Lin Lei, Hao Zhang, Shaoquan Zhang, Chengzhi Deng, Fan Li, Shengqian Wang","doi":"10.1117/12.2665779","DOIUrl":null,"url":null,"abstract":"Compared with traditional remote sensing images, hyperspectral images have the advantages of high spectral resolution, combining images with spectrum, and continuous spectrum. The phenomenon of mixed pixels in hyperspectral images seriously affects the accuracy of distinguishing objects on the ground, and has always been an important problem that hinders the further development of this technology. The most effective way to solve the mixed pixel problem is to perform mixed pixel unmixing. The purpose of hyperspectral unmixing is to obtain pure spectrum (endmembers) and their corresponding proportions (abundance). The nonnegative matrix factorization (NMF) technique has been widely adopted in the hyperspectral images unmixing problem due to its own advantages. The NMF method based on sparsity constraint can achieve better unmixing effect because of fully using of the sparse characteristic of the data. However, the unmixing model based on the sparse NMF still has shortcomings. Hyperspectral images contain a large amount of geometric structural information, which is not considered by most existing sparse NMF methods. To address those shortcomings, new regularization terms and weights can be introduced into the NMF model to better promote the unmixing performance. To solve this problem, a novel unmixing algorithm named spatial-spectral graph regularized sparse non-negative matrix factorization (SSGNMF) algorithm is proposed in this paper. Most of the sparse constrained unmixing algorithms have the problem of insufficient prior representation of abundance sparsity and using of spatial information insufficiently. On the one hand, the model of SSGNMF introduces graph regularization to preserve high-dimensional spatial information in hyperspectral images. On the other hand, the spatial weighting factor enables more spatial information to be incorporated into the unmixing model, and the spectral weighting factor can promote row sparsity of abundance matrices. By comparing with other classical algorithms, simulated and real hyperspectral data experimental results demonstrate that the introduction of dual weights and graph regularization can improve the unmixing effect, which verifies the validity of this algorithm. In addition, the experimental results also show that the graph regularization term and dual weights introduced in the NMF model in this paper can indeed promote the hyperspectral image unmixing performance well.","PeriodicalId":258680,"journal":{"name":"Earth and Space From Infrared to Terahertz (ESIT 2022)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth and Space From Infrared to Terahertz (ESIT 2022)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2665779","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Compared with traditional remote sensing images, hyperspectral images have the advantages of high spectral resolution, combining images with spectrum, and continuous spectrum. The phenomenon of mixed pixels in hyperspectral images seriously affects the accuracy of distinguishing objects on the ground, and has always been an important problem that hinders the further development of this technology. The most effective way to solve the mixed pixel problem is to perform mixed pixel unmixing. The purpose of hyperspectral unmixing is to obtain pure spectrum (endmembers) and their corresponding proportions (abundance). The nonnegative matrix factorization (NMF) technique has been widely adopted in the hyperspectral images unmixing problem due to its own advantages. The NMF method based on sparsity constraint can achieve better unmixing effect because of fully using of the sparse characteristic of the data. However, the unmixing model based on the sparse NMF still has shortcomings. Hyperspectral images contain a large amount of geometric structural information, which is not considered by most existing sparse NMF methods. To address those shortcomings, new regularization terms and weights can be introduced into the NMF model to better promote the unmixing performance. To solve this problem, a novel unmixing algorithm named spatial-spectral graph regularized sparse non-negative matrix factorization (SSGNMF) algorithm is proposed in this paper. Most of the sparse constrained unmixing algorithms have the problem of insufficient prior representation of abundance sparsity and using of spatial information insufficiently. On the one hand, the model of SSGNMF introduces graph regularization to preserve high-dimensional spatial information in hyperspectral images. On the other hand, the spatial weighting factor enables more spatial information to be incorporated into the unmixing model, and the spectral weighting factor can promote row sparsity of abundance matrices. By comparing with other classical algorithms, simulated and real hyperspectral data experimental results demonstrate that the introduction of dual weights and graph regularization can improve the unmixing effect, which verifies the validity of this algorithm. In addition, the experimental results also show that the graph regularization term and dual weights introduced in the NMF model in this paper can indeed promote the hyperspectral image unmixing performance well.
空间谱图正则化稀疏非负矩阵分解高光谱解混
与传统遥感影像相比,高光谱影像具有光谱分辨率高、影像与光谱结合、连续光谱等优点。高光谱图像中的混合像元现象严重影响了地面目标的识别精度,一直是阻碍该技术进一步发展的重要问题。解决混合像元问题最有效的方法是进行混合像元解混。高光谱解混的目的是获得纯光谱(端元)及其相应的比例(丰度)。非负矩阵分解(NMF)技术以其自身的优点在高光谱图像解混问题中得到了广泛的应用。基于稀疏性约束的NMF方法充分利用了数据的稀疏特性,可以获得较好的解混效果。然而,基于稀疏NMF的解混模型仍然存在不足。高光谱图像包含大量的几何结构信息,这是现有的稀疏NMF方法没有考虑到的。为了解决这些缺点,可以在NMF模型中引入新的正则化项和权值,以更好地提高解混性能。针对这一问题,本文提出了一种新的解混算法——空间谱图正则化稀疏非负矩阵分解(SSGNMF)算法。大多数稀疏约束解混算法存在丰度稀疏度先验表示不足和空间信息利用不足的问题。一方面,SSGNMF模型引入图正则化来保持高光谱图像中的高维空间信息;另一方面,空间加权因子可以使解混模型包含更多的空间信息,谱加权因子可以提高丰度矩阵的行稀疏性。通过与其他经典算法的比较,仿真和真实高光谱数据实验结果表明,引入对重权和图正则化可以提高解混效果,验证了该算法的有效性。此外,实验结果还表明,本文在NMF模型中引入的图正则化项和对偶权确实可以很好地提高高光谱图像的解混性能。
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