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Hyperspectral data unmixing with graph-based regularization

Rita Ammanouil, Andre Ferrari, Cedric Richard
2015 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)  
A graph-based Total Variation framework is incorporated within the unmixing problem.  ...  This work proposes to solve the unmixing problem in a graph setting.  ...  Using tools of discrete calculus on graphs [4] , we penalize the discrepancies between the estimated spectra of connected pixels via a graph-based Total Variation.  ... 
doi:10.1109/whispers.2015.8075377 dblp:conf/whispers/AmmanouilFR15 fatcat:qba4dgvqnfa3rodixbgnnd7wpq

A graph Laplacian regularization for hyperspectral data unmixing

Rita Ammanouil, Andre Ferrari, Cedric Richard
2015 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
This paper introduces a graph Laplacian regularization in the hyperspectral unmixing formulation.  ...  Finally, simulations conducted on synthetic and real data illustrate the effectiveness of the graph Laplacian regularization with respect to other classical regularizations for hyperspectral unmixing.  ...  The proposed strategy is closely related to the work in [10] where the authors use a Total Variation (TV) regularization on top of sparse 1-norm regularized unmixing.  ... 
doi:10.1109/icassp.2015.7178248 dblp:conf/icassp/AmmanouilFR15 fatcat:37t2fjwzozatlciqvirntin7sa

A graph Laplacian regularization for hyperspectral data unmixing [article]

Rita Ammanouil, André Ferrari, Cédric Richard
2014 arXiv   pre-print
Finally, simulations conducted on synthetic data illustrate the effectiveness of the graph Laplacian regularization with respect to other classical regularizations for hyperspectral unmixing.  ...  This paper introduces a graph Laplacian regularization in the hyperspectral unmixing formulation.  ...  The proposed strategy is closely related to the work in [8] where the authors use a Total Variation (TV) regularization on top of sparse 1-norm regularized unmixing.  ... 
arXiv:1410.3699v1 fatcat:gketmi4oprfefmi6duci6itlym

Table of contents

2021 IEEE Transactions on Geoscience and Remote Sensing  
Koner 2833 (Contents Continued on Page 2700) 3326 Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization ............................................. ............................  ...  Zhang 3292 Hyperspectral Image Restoration via Global L 1−2 Spatial-Spectral Total Variation Regularized Local Low-Rank Tensor Recovery .................................................................  ... 
doi:10.1109/tgrs.2021.3063896 fatcat:miqrb4or7jbujhr2aqlvhxq3oi

Table of contents

2019 IEEE Transactions on Image Processing  
Yuen 2976 Nonconvex-Sparsity and Nonlocal-Smoothness-Based Blind Hyperspectral Unmixing ..................................... ...........................................................................  ...  Zhang 2908 Tchebichef and Adaptive Steerable-Based Total Variation Model for Image Denoising .................................... .......................................................................  ... 
doi:10.1109/tip.2019.2905936 fatcat:xzdsx3tjbjfohhebapnp6gtb4u

Sparsity Constrained Distributed Unmixing of Hyperspectral Data [article]

Sara Khoshsokhan, Roozbeh Rajabi, Hadi Zayyani
2019 arXiv   pre-print
One of the constraints which was added to NMF is sparsity, that was regularized by Lq norm. In this paper, a new algorithm based on distributed optimization is suggested for spectral unmixing.  ...  Simulation results based on defined performance metrics illustrate the advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods.  ...  Spatial information has been used in different ways in spectral unmixing [35] , including total variation spatial regularization for sparse hyperspectral unmixing [36] .  ... 
arXiv:1902.07593v1 fatcat:urrrfbki5jeddau4qx62xcvz4e

An Overview on Linear Unmixing of Hyperspectral Data

Jiaojiao Wei, Xiaofei Wang
2020 Mathematical Problems in Engineering  
A single pixel that leads to a hyperspectral remote sensing image usually contains more than one feature coverage type, resulting in a mixed pixel.  ...  For the problem of unmixing of mixed pixels in hyperspectral images (HSIs), the linear mixing model can model the mixed pixels well.  ...  unmixing based on NMF is based on blind source separation theory.  ... 
doi:10.1155/2020/3735403 fatcat:ijkjzzp6lbavhfhkyx7rwnxngy

A blind spectral unmixing in wavelet domain

Vijayashekhar Ss, Jignesh Shashikant Bhatt
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Experiments are conducted on synthetic and three real benchmark hyperspectral data AVIRIS Cuprite, HYDICE Urban, and AVIRIS Jasper Ridge.  ...  Spectrally dense and overlapped hyperspectral data is represented using biorthogonal wavelet bases that yield a compact linear mixing model in the wavelet domain.  ...  Hence considering unmixing as a combinatorial problem, efficient techniques based on sparsity-induced regularizers have been investigated.  ... 
doi:10.1109/jstars.2021.3116698 fatcat:hzp5rgv7l5hs7btztvurst5ggi

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Pedram Ghamisi, Naoto Yokoya, Jun Li, Wenzhi Liao, Sicong Liu, Javier Plaza, Behnood Rasti, Antonio Plaza
2017 IEEE Geoscience and Remote Sensing Magazine  
This paper offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is categorized into seven broad topics: classification, spectral unmixing, dimensionality  ...  reduction, resolution enhancement, hyperspectral image denoising and restoration, change detection, and fast computing.  ...  total variation, and nonlocal total variation [200, 201] .  ... 
doi:10.1109/mgrs.2017.2762087 fatcat:6ezzye7yyvacbouduqv2f2c7gi

Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independence [article]

E.M.M.B. Ekanayake, H.M.H.K. Weerasooriya, D.Y.L. Ranasinghe, S. Herath, B. Rathnayake, G.M.R.I. Godaliyadda, M.P.B. Ekanayake, H.M.V.R. Herath
2021 arXiv   pre-print
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.  ...  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  ...  [43] , robust collaborative NMF (R-CoNMF) [44] , Subspace Structure Regularized NMF (SSRNMF) [45] , graph regularized NMF (GNMF) [46] and Projection-Based NMF (PNMF) [47] are some customary NMF-based  ... 
arXiv:2003.01041v5 fatcat:jys4xvzs4vepddyflgttqbcbby

Machine learning based hyperspectral image analysis: A survey [article]

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
2019 arXiv   pre-print
This paper reviews and compares recent machine learning-based hyperspectral image analysis methods published in literature.  ...  The image analysis tasks considered are land cover classification, target detection, unmixing, and physical parameter estimation.  ...  There is also a non-regularization based sparse endmember extraction and unmixing technique in literature.  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

Spectral-Spatial Constrained Nonnegative Matrix Factorization for Spectral Mixture Analysis of Hyperspectral images

Ge Zhang, Shaohui Mei, Yan Feng, Qian Du
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
state-of-theart NMF-based unmixing algorithms.  ...  Unfortunately, most of the NMF-based unmixing methods can easily lead to an unsuitable solution, due to inadequate mining of spatial and spectral information and the influence of outliers and noise.  ...  modules based on boundary, center, and total variation (TV).  ... 
doi:10.1109/jstars.2021.3092566 fatcat:b5br6f6q45a2pcrq3xqvxgjam4

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing [article]

Danfeng Hong and Wei He and Naoto Yokoya and Jing Yao and Lianru Gao and Liangpei Zhang and Jocelyn Chanussot and Xiao Xiang Zhu
2021 arXiv   pre-print
Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS).  ...  However, with the ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges on reducing the burden of manual labor and improving efficiency.  ...  XX = I; • Total Variation [63]: Ψ(X) = r i=1 (H h X i ) 2 + (H v X i ) 2 1 , s.t.  ... 
arXiv:2103.01449v1 fatcat:jvo4pr5atvfb5kohpslvkhhmky

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 1902-1914 Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry.  ...  ., +, TIP 2020 3719-3733 Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Table of contents

2019 IEEE Transactions on Image Processing  
Flierl 2731 Radar Imaging, Remote Sensing, and Geophysical Imaging Nonconvex-Sparsity and Nonlocal-Smoothness-Based Blind Hyperspectral Unmixing ..................................... ..................  ...  Li 2882 Tchebichef and Adaptive Steerable-Based Total Variation Model for Image Denoising .................................... ..........................................................................  ... 
doi:10.1109/tip.2019.2905937 fatcat:hdjjnnguk5dera2si44df5zude
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