[PDF] Top 20 Matrix Cofactorization for Joint Unmixing and Classification of Hyperspectral Images
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Matrix Cofactorization for Joint Unmixing and Classification of Hyperspectral Images
... spectral unmixing, cofac- torization, hyperspectral ...increase of available remote sensing images, many methods have been proposed to extract infor- mation from such specific ...particular ... Voir le document complet
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Matrix cofactorization for joint representation learning and supervised classification : application to hyperspectral image analysis
... classification and representation learning are two widely used classes of methods to analyze multivariate ...a matrix cofactorization ...factorization matrix problem and a term ... Voir le document complet
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Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images
... sensing images is richer than this simple state- ...conventional hyperspectral unmixing approach can leverage the spatial information to help for spectral ...contexts. For instance, ... Voir le document complet
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Joint Bayesian Hyperspectral Unmixing for change detection
... variation of HSIs for CD. The authors of [4] have proposed a Linear Mixture Model for endmember and abundance estimation of each image ...vectors and performing a ... Voir le document complet
5
A Bayesian model for joint unmixing and robust classification of hyperspectral image
... Supervised classification and spectral unmixing are two methods to extract information from hyperspectral ...groups of statistically similar abundance ...prior for the ... Voir le document complet
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A Bayesian model for joint unmixing and robust classification of hyperspectral image
... INTRODUCTION Hyperspectral images are mainly interpreted via two widely used techniques, namely spectral unmixing (SU) and ...image and the corresponding proportions within each pixel ... Voir le document complet
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A new Bayesian unmixing algorithm for hyperspectral images mitigating endmember variability
... algorithm for hyperspectral image unmixing accounting for endmember ...tion of random endmembers to take into account endmember variability in the ...coefficients of this linear ... Voir le document complet
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Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image
... way of defining MRFs is applied to the joint unmixing and segmentation algorithm of ...implicit classification is carried out by assigning hidden discrete vari- ables or class ... Voir le document complet
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A new Bayesian unmixing algorithm for hyperspectral images mitigating endmember variability
... algorithm for hyperspectral image unmixing accounting for endmember ...tion of random endmembers to take into account endmember variability in the ...coefficients of this linear ... Voir le document complet
6
Residual Component Analysis of Hyperspectral Images - Application to Joint Nonlinear Unmixing and Nonlinearity Detection
... The hyperspectral image to be analyzed is partitioned into homogeneous regions in which the nonlinearities share the same ...image classification, modeled by labels whose spatial dependencies follow a ... Voir le document complet
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Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery
... In hyperspectral imagery, ICA has also been envisaged ...[16] and [23], ICA may perform poorly for LSMA due to the strong dependence between the different abundances ...the hyperspectral ... Voir le document complet
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Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image
... theory and allows the structure of interest to be preserved independently of their contrasts while removing small mean- ingless structures ...on hyperspectral images since the complete ... Voir le document complet
13
Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model
... b and w, the joint prior distri- bution of the θ can be expressed as f (θ) = f (Z)f (M)f (σ 2 )f (b|σ b 2 , w)f (σ 2 b )f ...expressions for the standard Bayesian estimators associated with ... Voir le document complet
6
Joint Bayesian Hyperspectral Unmixing for change detection
... variation of HSIs for CD. The authors of [4] have proposed a Linear Mixture Model for endmember and abundance estimation of each image ...vectors and performing a ... Voir le document complet
6
Joint Anomaly Detection and Spectral Unmixing for Planetary Hyperspectral Images
... number of methods assume that at least one pure instance per endmember is included in the data ...identification of the simplex that encompasses data [23], ...combination of some points is included ... Voir le document complet
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Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization
... line of investigation concerns the rele- vance of lifting the nonnegative constraint on the outlier term ...majority of which assume the nonlinearity term to be ...decrease of reflectance. ... Voir le document complet
11
Band selection in RKHS for fast nonlinear unmixing of hyperspectral images
... INTRODUCTION Hyperspectral images (HI) consist of hundreds or even thousands of contiguous spectral samples ranging from the visible to the near in- frared portions of the light ... Voir le document complet
7
Band selection in RKHS for fast nonlinear unmixing of hyperspectral images
... One of the problems in practical implementation of unmixing algorithms is the profusion of spectral bands generated in the ac- quisition process, which leads to high computational ...true ... Voir le document complet
6
Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization
... case of the ...each of the parameters M, A and R in ...value of the other parameters and such that the objective function is ...non-convexity of the objective function J (M, A, ... Voir le document complet
12
Frontiers in Spectral-Spatial Classification of Hyperspectral Images
... Results and Discussions: From Tables XIV-XVI, the following points can be observed: By only utilizing the spectral information, SRC generally delivers the worst classification ...SBSDM, and SAS ... Voir le document complet
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