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An Overview on Linear Unmixing of Hyperspectral Data

Jiaojiao Wei, Xiaofei Wang
2020 Mathematical Problems in Engineering  
factorization (NMF), Bayesian method, and sparse unmixing.  ...  Through the collation of nearly five years of the literature, this paper introduces the development status and problems of linear unmixing models from four aspects: geometric method, nonnegative matrix  ...  Most of the unmixing methods based on nonnegative matrix factorization do not fully use the spatial information and spectral information. erefore, it is necessary to deeply explore and fully use all kinds  ... 
doi:10.1155/2020/3735403 fatcat:ijkjzzp6lbavhfhkyx7rwnxngy

Unmixing of Hyperspectral Data Using Robust Statistics-based NMF [article]

Roozbeh Rajabi, Hassan Ghassemian
2012 arXiv   pre-print
In this paper using of robust statistics-based nonnegative matrix factorization (RNMF) for spectral unmixing of hyperspectral data is investigated.  ...  Mixed pixels are presented in hyperspectral images due to low spatial resolution of hyperspectral sensors.  ...  In this paper robust statistics based nonnegative matrix factorization (RNMF) method has been used for spectral unmixing of hyperspectral images.  ... 
arXiv:1212.0888v1 fatcat:w4wpwmqqkndypldg3f7t7ahcke

Multilinear spectral unmixing of hyperspectral multiangle images

M. A. Veganzones, J. Cohen, R. Cabrai Farias, R. Marrero, J. Chanussot, P. Comon
2015 2015 23rd European Signal Processing Conference (EUSIPCO)  
Index Terms-Multilinear spectral unmixing, hyperspectral multiangle images, multiway analysis, Canonical Polyadic, nonnegative tensor decomposition.  ...  By means of spectral unmixing it is possible to decompose a hyperspectral image in its spectral components, the so-called endmembers, and their respective fractional spatial distributions, so-called abundance  ...  The CP decomposition could be understood as an extension of the linear unmixing of 2-way (spatial and spectral) hyperspectral data [6] to the multi-linear unmixing of multi-way (more than two) hyperspectral  ... 
doi:10.1109/eusipco.2015.7362482 dblp:conf/eusipco/VeganzonesCFMCC15 fatcat:knzlu7cykvesff2ku75zmxetpi

Area-Correlated Spectral Unmixing Based on Bayesian Nonnegative Matrix Factorization

Xiawei Chen, Jing Yu, Weidong Sun
2013 Open Journal of Applied Sciences  
To solve the problem of the spatial correlation for adjacent areas in traditional spectral unmixing methods, we propose an area-correlated spectral unmixing method based on Bayesian nonnegative matrix  ...  Experimental results demonstrate the effectivity and efficiency of the proposed method both for synthetic and real hyperspectral images, and it can provide a useful tool for spatial correlation and comparation  ...  A new trend is to apply nonnegative matrix factorization to the spectral unmixing, since all of elements in the endmember matrix and the abundance matrix are nonnegative [6, 7] .  ... 
doi:10.4236/ojapps.2013.31b009 fatcat:si5g3wjkyvhqbooqy7a3umrefi

Hyperspectral Data Unmixing Using GNMF Method and Sparseness Constraint [article]

Roozbeh Rajabi, Hassan Ghassemian
2013 arXiv   pre-print
Due to nonnegativity constraint on abundance fractions, nonnegative matrix factorization methods (NMF) have been widely used for solving spectral unmixing problem.  ...  In this paper we have used graph regularized (GNMF) method with sparseness constraint to unmix hyperspectral data.  ...  Due to nonnegativity constraint in linear mixing model, nonnegative matrix factorization (NMF) has been widely used for solving spectral unmixing problem for example in [11] and [12] .  ... 
arXiv:1307.0129v1 fatcat:heu2esptr5hllgsmsbusxvymxe

A New Multiplicative Nonnegative Matrix Factorization Method For Unmixing Hyperspectral Images Combined With Multispectral Data

Yannick Deville, Shahram Hosseini, Moussa Sofiane Karoui, Benkouider Yasmine Kheira
2018 Zenodo  
As the analyzed data are nonnegative, nonnegative matrix factorization (NMF) [2] methods, consisting in factorizing a nonnegative matrix into a product of two other nonnegative matrices, are an interesting  ...  Ideally, this sparse and nonnegative matrix models the transformation between the spectral responses of hyperspectral and multispectral sensors.  ... 
doi:10.5281/zenodo.1159617 fatcat:nplvd4juoree7j35hiw4yqqtna

Graph Regularized Nonnegative Matrix Factorization for Hyperspectral Data Unmixing [article]

Roozbeh Rajabi, Mahdi Khodadadzadeh, Hassan Ghassemian
2011 arXiv   pre-print
This paper examines the applicability of a recently developed algorithm called graph regularized nonnegative matrix factorization (GNMF) for this aim.  ...  Spectral unmixing is an important tool in hyperspectral data analysis for estimating endmembers and abundance fractions in a mixed pixel.  ...  In this paper graph regularized nonnegative matrix factorization method (GNMF) has been used for spectral mixture analysis of hyperspectral imagery.  ... 
arXiv:1111.0885v1 fatcat:shosbxft5ngkfd2cjhp2umji7u

Multilayer Structured NMF for Spectral Unmixing of Hyperspectral Images [article]

Roozbeh Rajabi, Hassan Ghassemian
2015 arXiv   pre-print
In this paper multilayer NMF has been used to improve the results of NMF methods for spectral unmixing of hyperspectral data under the linear mixing framework.  ...  One of the challenges in hyperspectral data analysis is the presence of mixed pixels. Mixed pixels are the result of low spatial resolution of hyperspectral sensors.  ...  Another class of algorithms that are used for spectral unmixing purposes are methods based on nonnegative matrix factorization (NMF).  ... 
arXiv:1506.01596v1 fatcat:46zadbta7fbw7hth4vdare4uqq

Detection And Area Estimation For Photovoltaic Panels In Urban Hyperspectral Remote Sensing Data By An Original Nmf-Based Unmixing Method

Moussa Sofiane Karoui, Fatima zohra Benhalouche, Yannick Deville, Khelifa Djerriri, Xavier Briottet, Arnaud Le Bris
2018 IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium  
Linear Spectral Unmixing (LSU) is one of the most used techniques for processing hyperspectral data.  ...  Index Terms-Hyperspectral imaging, hyperspectral unmixing, partial/informed nonnegative matrix factorization, detection and area estimation, photovoltaic panels INTRODUCTION The number of operating renewable  ... 
doi:10.1109/igarss.2018.8518204 dblp:conf/igarss/KarouiBDDBB18 fatcat:pezi576hgbdktos75edndyfosq

Nonnegative Tensor CP Decomposition of Hyperspectral Data

Miguel A. Veganzones, Jeremy E. Cohen, Rodrigo Cabral Farias, Jocelyn Chanussot, Pierre Comon
2016 IEEE Transactions on Geoscience and Remote Sensing  
Overall, the estimated spatial factors are meaningful and the qualitative visual assessment encourages us to further investigate the use of the nonnegative CP decomposition as a multilinear "blind" spectral  ...  Hyperspectral images (HSI) are usually analysed as a nonnegative matrix, X ∈ R N ×D + , where N denotes the number of pixels in the image and D denotes the number of spectral bands.  ... 
doi:10.1109/tgrs.2015.2503737 fatcat:oaojnm72d5bhbdaxccb4uapwfu

Graph regularized coupled spectral unmixing for change detection

Naoto Yokoya, Xiaoxiang Zhu
2015 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)  
The problem is formulated in the framework of coupled nonnegative matrix factorization.  ...  This paper presents a methodology of coupled spectral unmixing for multitemporal hyperspectral data analysis.  ...  Coupled spectral unmixing for multitemporal hyperspectral data is formulated in the framework of coupled nonnegative matrix factorization [3] using graph regularization on spectral correlation between  ... 
doi:10.1109/whispers.2015.8075494 dblp:conf/whispers/YokoyaZ15 fatcat:mjt4ralfurenpjia3ww3l3u7da

Multilayer manifold and sparsity constrainted nonnegative matrix factorization for hyperspectral unmixing

Zhenqiu Shu, Jun Zhou, Lei Tong, Xiao Bai, Chunxia Zhao
2015 2015 IEEE International Conference on Image Processing (ICIP)  
In this paper, we propose a novel method, Multilayer Manifold and Sparsity constrained Nonnegative Matrix Factorization (MMSNMF), for hyperspectral unmixing.  ...  Recently, Nonnegative Matrix Factorization (NMF) has been widely applied to solve the hyperspectral unmixing problem because of its plausible physical interpretation.  ...  Nonnegative Matrix Factorization (NMF) [4, 5] is a popular linear unmixing method to deal with the blind source separation (BSS) problem, which has been widely applied to hyperspectral unmixing.  ... 
doi:10.1109/icip.2015.7351186 dblp:conf/icip/ShuZT0Z15 fatcat:47qno3hr5zalldbevjauwlalhy

Canonical polyadic decomposition of hyperspectral patch tensors

M. A. Veganzones, J. E. Cohen, R. Cabral Farias, K. Usevich, L. Drumetz, J. Chanussot, P. Comon
2016 2016 24th European Signal Processing Conference (EUSIPCO)  
By means of spectral unmixing it is possible to decompose a hyperspectral image in its spectral components, the so-called endmembers, and their respective fractional spatial distributions, so-called abundance  ...  Index Terms-Spectral unmixing, extended linear mixing model, Canonical Polyadic, nonnegative tensor decomposition, patch tensor.  ...  Given a nonnegative hyperspectral image, X ∈ R N ×L + , where N denotes the number of pixels and L the number of spectral bands, spectral unmixing (SU) aims at estimating the spectral signatures of the  ... 
doi:10.1109/eusipco.2016.7760634 dblp:conf/eusipco/VeganzonesCFUDC16 fatcat:nghpm5is5nht7fyacwdee5kl4m

Coupled non-negative matrix factorization (CNMF) for hyperspectral and multispectral data fusion: Application to pasture classification

Naoto Yokoya, Takehisa Yairi, Akira Iwasaki
2011 2011 IEEE International Geoscience and Remote Sensing Symposium  
Coupled non-negative matrix factorization (CNMF) is introduced for hyperspectral and multispectral data fusion.  ...  The CNMF fused data have little spectral distortion while enhancing spatial resolution of all hyperspectral band images owing to its unmixing based algorithm.  ...  Given a non-negative matrix V, NMF looks for two nonnegative matrix factors W and H such that V = WH.  ... 
doi:10.1109/igarss.2011.6049465 dblp:conf/igarss/YokoyaYI11 fatcat:4zpk7efix5hh5d5oyt5geo2k3u

Sparsity Constrained Graph Regularized NMF for Spectral Unmixing of Hyperspectral Data

Roozbeh Rajabi, Hassan Ghassemian
2014 Journal of the Indian Society of Remote Sensing  
Due to nonnegativity constraint on abundance fractions, nonnegative matrix factorization methods (NMF) have been widely used for solving spectral unmixing problem.  ...  Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Mixed pixels are pixels containing more than one distinct material called endmembers.  ...  Specifically in AVIRIS Cuprite experiment, the results using the proposed method is about 10% improved in terms of rmsSAD.  ... 
doi:10.1007/s12524-014-0408-2 fatcat:5dtjm3rxrva33irn6foagxaok4
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