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Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery
2017
IEEE Transactions on Geoscience and Remote Sensing
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Extended from NMF based methods, a matrix-vector nonnegative tensor factorization (NTF) model is proposed in this paper for spectral unmixing. ...
Compared with matrix format, the third-order tensor is more natural to represent a hyperspectral data cube as a whole, by which the intrinsic structure of hyperspectral imagery can be losslessly retained ...
Inspired by NMF based unmixing, we add nonnegativity into matrix-vector tensor factorization. ...
doi:10.1109/tgrs.2016.2633279
fatcat:v7ylggln7namdkiiqaa6bv3a7e
Paper evolution graph: Multi-view structural retrieval for academic literature
[article]
2017
arXiv
pre-print
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In this work, we aim to uncover the relationships of the retrieval results and propose a method for building structural retrieval results for academic literatures, which we call a paper evolution graph ...
First, the papers are soft-clustered into communities via metagraph factorization during which the topic distribution of each paper is obtained. ...
PEG of the query-paper "Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing." ...
arXiv:1711.08913v1
fatcat:2z4vywjdpbgrjhwcok35zfqpuy
Sparsity-Constrained Coupled Nonnegative Matrix-Tensor Factorization for Hyperspectral Unmixing
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Recently, matrix-vector nonnegative tensor factorization (MV-NTF) was proposed for unmixing to avoid structure information loss, which is caused by the HSI cube unfolding in nonnegative matrix factorization ...
In this paper, we propose a sparsityconstrained coupled nonnegative matrix-tensor factorization (SC-NMTF) model for unmixing, wherein MV-NTF and NMF are subtly coupled by sharing endmembers and abundances ...
For more information, see https://creativecommons.org/licenses/by/4.0/. ...
doi:10.1109/jstars.2020.3019706
fatcat:6ntfniu5nbev7fts3zle3f4toy
NMF-SAE: An Interpretable Sparse Autoencoder for Hyperspectral Unmixing
2021
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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In this paper, we combine the advantages of both modelbased and learning-based methods and propose a nonnegative matrix factorization (NMF) inspired sparse autoencoder (NMF-SAE) for hyperspectral unmixing ...
Hyperspectral unmixing is an important tool to learn the material constitution and distribution of a scene. ...
In order to tackle the information loss when converting the three-dimensional HSI into a two-dimensional matrix, nonnegative tensor factorization (NTF) was also adopted for unmixing task [7] . ...
doi:10.1109/icassp39728.2021.9414084
fatcat:uinck7nqkvbj7k2aq7m6r3zxpi
Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independence
[article]
2021
arXiv
pre-print
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As a promising step toward finding an optimum constraint to extract endmembers, this paper presents a novel blind HU algorithm, referred to as Kurtosis-based Smooth Nonnegative Matrix Factorization (KbSNMF ...
Over the past few decades, many attempts have focused on imposing auxiliary constraints on the conventional nonnegative matrix factorization (NMF) framework in order to effectively unmix these mixed spectra ...
In the paper, Matrix-Vector NTF for Blind Unmixing of Hyperspectral Imagery (MVNTF) [48] , the authors have formalized a novel way of unmixing while preserving the spatial information by factorizing hyper ...
arXiv:2003.01041v5
fatcat:jys4xvzs4vepddyflgttqbcbby
Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization
2011
IEEE Transactions on Geoscience and Remote Sensing
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In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. ...
Index Terms Hyperspectral unmixing; Nonnegative matrix factorization; Sparse coding, L 1/2 regularizer Y. Qian is with the Institute ...
For general matrix factorization problems, traditional matrix computation tools such as singular vector decomposition (SVD), QR decomposition and LU factorization can be used. ...
doi:10.1109/tgrs.2011.2144605
fatcat:gih5tznrenfjliivcndhhzzrje
Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery
2021
Wireless Communications and Mobile Computing
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Sparse unmixing is an important technique for hyperspectral data analysis. ...
Most sparse unmixing algorithms underutilize the spatial and spectral information of the hyperspectral image, which is unfavourable for the accuracy of endmember identification and abundance estimation ...
In the future, we will extend the constraint strategy to blind unmixing and further explore the spectral-spatial structure information of the hyperspectral data cube represented by a third-order tensor ...
doi:10.1155/2021/9374908
fatcat:dnmpf6i7ujanzeg6g7sg2psy5e
Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing Incorporating Endmember Independence
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Furthermore, A new architecture has recently emerged for blind unmixing under the premise Nonnegative Tensor factorization (NTF). ...
In the paper, Matrix-Vector NTF for Blind Unmixing of Hyperspectral Imagery (MVNTF) [52] , the authors have formalized a novel way of unmixing while preserving the spectral and spatial information by ...
doi:10.1109/jstars.2021.3126664
fatcat:pwdnsv23tfchnbym6wjrgkv2qm
Dynamical Spectral Unmixing of Multitemporal Hyperspectral Images
2016
IEEE Transactions on Image Processing
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In this paper, we consider the problem of unmixing a time series of hyperspectral images. We propose a dynamical model based on linear mixing processes at each time instant. ...
The performance of the proposed approach is demonstrated on synthetic and real multitemporal hyperspectral images. ...
Specifically, if one cancels the additive noise term in (3), equations (5) and (3) combine to write X k = S 0 ψ k A k + E k (6) which is known as the nonnegative tensor factorization 1 (NTF1) under nonnegativity ...
doi:10.1109/tip.2016.2562562
pmid:27164590
fatcat:xwjss2vs7ffqpotujc5w3t6ony
Multi-Resolution Beta-Divergence NMF for Blind Spectral Unmixing
[article]
2021
arXiv
pre-print
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To perform blind source separation using observations with different resolutions, a standard approach is to use coupled nonnegative matrix factorizations (NMF). ...
We show on numerical experiments that the MU are able to obtain high resolutions in both dimensions on two applications: (1) blind unmixing of audio spectrograms: to the best of our knowledge, this is ...
Selva, “Mtf-
coupled nonnegative matrix–tensor factorization for hyperspectral un- tailored multiscale fusion of high-resolution ms and pan imagery,”
mixing,” IEEE Journal of Selected ...
arXiv:2007.03893v3
fatcat:whbh53miajfa3l6hxk7mk3theq
2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57
2019
IEEE Transactions on Geoscience and Remote Sensing
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., Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix; TGRS Aug. 2019 6212-6225 Hu, C., see Zhang, M., TGRS Sept. 2019 6666-6674 Hu, C., Zhang, ...
6607 Hu, F., Wu, J., Chang, L., and Hanssen, R.F., Incorporating Temporary Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for ...
., +, TGRS Oct. 2019 7718-7730 Spectral-Spatial Robust Nonnegative Matrix Factorization for Hyperspectral Unmixing. ...
doi:10.1109/tgrs.2020.2967201
fatcat:kpfxoidv5bgcfo36zfsnxe4aj4
Spatial Low-Rank Tensor Factorization and Unmixing of Hyperspectral Images
2021
Computers
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This work presents a method for hyperspectral image unmixing based on non-negative tensor factorization. ...
The results of this method are compared against other approaches based on non-negative matrix and tensor factorization. ...
of Puerto Rico, Mayaguez for their support in the implementation of this project. ...
doi:10.3390/computers10060078
fatcat:lxs3357ml5h7ha6npkr6le2yfy
Hyperspectral Unmixing via Low-Rank Representation with Space Consistency Constraint and Spectral Library Pruning
2018
Remote Sensing
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Spectral unmixing is a popular technique for hyperspectral data interpretation. ...
However, the high mutual coherence of spectral libraries always affects the performance of sparse unmixing. In addition, the hyperspectral image has the special characteristics of space. ...
[30] Remote Sens. 2018, 10, 339 3 of 21 adopted the method called matrix-vector nonnegative tensor factorization for blind unmixing of hyperspectral imagery. Fan et al. ...
doi:10.3390/rs10020339
fatcat:t5n6xsoapneyvepkvvqxblvkfa
Multiple Clustering Guided Nonnegative Matrix Factorization for Hyperspectral Unmixing
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Recently, constrained nonnegative matrix factorization (NMF) has been demonstrated to be a powerful tool for spectral unmixing. ...
Spectral unmixing is an important technique for quantitatively analyzing hyperspectral remote sensing images. ...
MVNTF performs the unmixing tasks under the framework of nonnegative tensor factorization. VCA is a method based on the geometrical characteristic of HSIs. ...
doi:10.1109/jstars.2020.3020541
fatcat:ccbmoy4vzvfoneriuhh43jux54
Multiple-Priors Ensemble Constrained NonnegativeMatrix Factorization for Spectral Unmixing
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Index Terms-Barzilai-Borwein stepsize, hyperspectral unmixing, multiple-priors, nonnegative matrix factorization (NMF), variable splitting and augmented Lagrangian. ...
Nonnegative matrix factorization (NMF) is widely used in unmixing issue in recent years, because it can simultaneously estimate the endmembers and abundances. ...
He for generously sharing the code of TV-RSNMF method. ...
doi:10.1109/jstars.2020.2976602
fatcat:xib2qvbdmfgdxh5tglq2bmecni
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