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Nonnegative-Matrix-Factorization-Based Hyperspectral Unmixing With Partially Known Endmembers
2016
IEEE Transactions on Geoscience and Remote Sensing
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In this paper, we address the hyperspectral unmixing problem with partially known endmembers. ...
This is, however, not true for some unmixing tasks for which part of the endmember signatures may be known in advance. ...
These form the basis for the proposed method on hyperspectral unmixing with partially known prior knowledge. ...
doi:10.1109/tgrs.2016.2586110
fatcat:k7bn4smjmvf4fndpoeb22f5l24
A Projected Gradient-Based Algorithm To Unmix Hyperspectral Data
2012
Zenodo
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Under the constraints of non-negativity of A and S, (1) may be regarded as a general NMF problem which tries to factorize the non-negative matrix X into two nonnegative matrices A and S, while N represents ...
Different methods based on constrained nonnegative matrix factorization have been recently proposed to unmix hyperspectral data [10, 11, 12] . ...
doi:10.5281/zenodo.43026
fatcat:odtvhisy2ferrgnjwfomfpw42q
Using a Panchromatic Image to Improve Hyperspectral Unmixing
2020
Remote Sensing
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Then, in order to complete this first endmember set, a local approach using a constrained non-negative matrix factorization strategy, is proposed. ...
Our method, called Heterogeneity-Based Endmember Extraction coupled with Local Constrained Non-negative Matrix Factorization (HBEE-LCNMF), has several steps: a first set of endmembers is estimated based ...
Schaepman for providing us with the Basel database and answering our questions. We thank Philippe Deliot from ONERA for providing us with the Mauzac HS acquisition. ...
doi:10.3390/rs12172834
fatcat:keexmj4ejndxhiqdjwben3n2b4
A comparative analysis of GPU implementations of spectral unmixing algorithms
2011
High-Performance Computing in Remote Sensing
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Spectral unmixing is a very important task for remotely sensed hyperspectral data exploitation. ...
These two steps comprise a hyperspectral unmixing chain, which can be very time-consuming (particularly for high-dimensional hyperspectral images). ...
This decomposition is known as Gaussian elimination or the LU factorization (with partial row pivoting). ...
doi:10.1117/12.897329
fatcat:upuwl4uvyfabrkudo5nwez4gvy
Supervised Nonlinear Unmixing of Hyperspectral Images Using a Pre-image Methods
[chapter]
2020
New Concepts in Imaging: Optical and Statistical Models
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Spectral unmixing is an important issue to analyze remotely sensed hyperspectral data. ...
This involves the decomposition of each mixed pixel into its pure endmember spectra, and the estimation of the abundance value for each endmember. ...
In the spirit of these derivations, we suggest to consider kernels of the form κ(r i , r j ) = (1 − γ) r i Σ r j + γ κ (r i , r j ) (4.2) with κ (r i , r j ) a reproducing kernel, Σ a non-negative matrix ...
doi:10.1051/978-2-7598-2487-8-021
fatcat:bk5vck5u35gfbiqjtmrh24stbm
Supervised Nonlinear Unmixing of Hyperspectral Images Using a Pre-image Methods
2013
EAS Publications Series
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Spectral unmixing is an important issue to analyze remotely sensed hyperspectral data. ...
This involves the decomposition of each mixed pixel into its pure endmember spectra, and the estimation of the abundance value for each endmember. ...
In the spirit of these derivations, we suggest to consider kernels of the form κ(r i , r j ) = (1 − γ) r i Σ r j + γ κ (r i , r j ) (4.2) with κ (r i , r j ) a reproducing kernel, Σ a non-negative matrix ...
doi:10.1051/eas/1359019
fatcat:5kqo4dr45fdn5iof4y7bgs65ja
Fast Constrained Least Squares Spectral Unmixing Using Primal-Dual Interior-Point Optimization
2014
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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For large hyperspectral image data sets, the estimation of the abundance maps requires the resolution of a large-scale optimization problem subject to linear constraints such as non-negativity and sum ...
Abstract Hyperspectral data unmixing aims at identifying the components (endmembers) of an observed surface and at determining their fractional abundances inside each pixel area. ...
Actually, there is an increasing interest to joint estimation methods based either on non-negative source separation [5, 6] or constrained non-negative matrix factorization [7, 8] . ...
doi:10.1109/jstars.2013.2266732
fatcat:ipj63r3a6vb5hbaisjqo6uca3y
Hyperspectral Unmixing Based on Constrained Bilinear or Linear-Quadratic Matrix Factorization
2021
Remote Sensing
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These methods are based on bilinear or linear-quadratic matrix factorization with non-negativity constraints. Two types of algorithms are considered. ...
In this work, unsupervised hyperspectral unmixing methods, designed for the bilinear and linear-quadratic mixing models, are proposed. ...
Acknowledgments: The authors would like to thank the developers of the used literature algorithms for sharing their MATLAB implementations. ...
doi:10.3390/rs13112132
fatcat:valjref6sfcbjbtlrzytx2fxx4
Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data
2021
Remote Sensing
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In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. ...
Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. ...
NMF aims to decompose a given non-negative matrix into two non-negative factor matrices with low ranks, so that the minimum error between the product of these factor matrices and the original matrix is ...
doi:10.3390/rs13020268
fatcat:fdteqnmxnjdzfbw7x7vangyehi
Parallel unmixing of remotely sensed hyperspectral images on commodity graphics processing units
2011
Concurrency and Computation
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Spectral unmixing involves the separation of a pixel spectrum into its pure component endmember spectra, and the estimation of the abundance value for each endmember [4] . ...
For instance, the pixel vector labeled as 'vegetation' in Figure 1 may actually be a mixed pixel comprising a mixture of vegetation and soil, or different types of soil and vegetation canopies. ...
CUDA kernel ISRA that computes endmember abundances in each pixel of the hyperspectral image imposing the abundance non-negativity constraint. • Figure A2 shows the code for the Unmixing kernel used ...
doi:10.1002/cpe.1720
fatcat:y5nwiqbacvbz7oowntksw2cyom
A Hierarchical Sparsity Unmixing Method to Address Endmember Variability in Hyperspectral Image
2018
Remote Sensing
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With that in mind, this paper proposes a hierarchical weighted sparsity unmixing (HWSU) method to improve the separation of similar interclass endmembers. ...
With a low spectral resolution hyperspectral sensor, the signal recorded from a given pixel against the complex background is a mixture of spectral contents. ...
We would like to thank the editor and reviewers for their reviews that improved the content of this paper. ...
doi:10.3390/rs10050738
fatcat:f6fhdc7a2vhhfhwxds7zxrsks4
Real-time implementation of a full hyperspectral unmixing chain on graphics processing units
2011
Satellite Data Compression, Communications, and Processing VII
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Hyperspectral unmixing is a very important task for remotely sensed hyperspectral data exploitation. ...
In this paper, we develop a real-time implementation of a full unmixing chain for hyperspectral data on graphics processing units (GPUs). ...
This decomposition is known as Gaussian elimination or the LU factorization (with partial row pivoting). ...
doi:10.1117/12.892284
fatcat:giiksnxhbve7vazppxnaceyngq
Superpixel-Based Hyperspectral Unmixing with Regional Segmentation
2018
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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DEDICATION This work is dedicated to my great wife for her support and help during this period of my life. To my great parents for their prayers and support. Finally, to my beautiful daughter Zaina. ...
Given a non-negative matrix Y, NMF tries to find non-negative matrices S (Basis matrix) and A (encoding Matrix), such that Y ≈ SA. ...
This method only uses one of the smoothed images from the multiscale representation for spectral endmember extraction [38] . •
The Spatially Adaptive Constrained Non Negative Matrix Factorization for ...
doi:10.1109/igarss.2018.8518222
dblp:conf/igarss/AlkhatibV18
fatcat:ebpfobydubdoljs7toqa2bfwoe
Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independence
[article]
2021
arXiv
pre-print
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Hyperspectral unmixing (HU) has become an important technique in exploiting hyperspectral data since it decomposes a mixed pixel into a collection of endmembers weighted by fractional abundances. ...
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 ...
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
Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization
2006
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
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This research dealt with the unsupervised determination of the constituents and their fractional abundance in each pixel in a hyperspectral image using a constrained positive matrix factorization (cPMF ...
In hyperspectral imaging, hundreds of images are taken at narrow and contiguous spectral bands providing us with high spectral resolution spectral signatures that can be used to discriminate between objects ...
NNLS
Non-Negative Least Squares.
PMF
Positive Matrix Factorization.
NNMF
Non-Negative Matrix Factorization.
ALS
Alternating Least Squares. ...
doi:10.1117/12.667976
fatcat:yj3xl6cyqjg2ze4jtnuxdkymzi
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