[PDF] Top 20 Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model
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Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model
... presents a nonlinear mixing model for hyperspec- tral image ...proposed model assumes that the pixel reflectances are post-nonlinear functions of unknown pure ... Voir le document complet
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Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithm
... Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithm Yoann Altmann, Member, IEEE, Nicolas Dobigeon, Senior Member, IEEE, and Jean-Yves T[r] ... Voir le document complet
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Nonlinear unmixing of hyperspectral images: Models and algorithms
... considers a kernel-based approach for unsupervised nonlinear SU based on a nonlinear dimensionality reduction using a Gaussian process latent vari- able ... Voir le document complet
14
Estimating abundance fractions of materials in hyperspectral images by fitting a post-nonlinear mixing model
... ADMM algorithm in the convex case, which is known to converge to the global ...the algorithm may lead to a local minimum de- pending on the initial values for the variables ...the ... Voir le document complet
5
A Bayesian model for joint unmixing and robust classification of hyperspectral image
... Terms— Bayesian model, Markov random Field, super- vised learning, image ...INTRODUCTION Hyperspectral images are mainly interpreted via two widely used techniques, namely spectral ... Voir le document complet
6
Unsupervised Bayesian linear unmixing of gene expression microarrays
... context of hyperspectral imaging to solve similar prob- lems ...Most of these algorithms perform unmixing in a two step procedure where M is estimated first using an endmember ... Voir le document complet
21
Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model
... ONCLUSION A nonlinearity detector was presented for hyperspectral image ...if a pixel of a hyperspectral image is a linear combination of endmembers or ... Voir le document complet
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Robust nonnegative matrix factorization for nonlinear unmixing of hyperspectral images
... abundances) A = [a 1 , . . . , a P ] T in each observation [1]. Most of the hyperspectral unmixing algo- rithms proposed in the signal & image processing and geoscience ... Voir le document complet
5
Nonlinear hyperspectral unmixing using Gaussian processes
... in a hyperspectral ...from a set of nonlinearly mixed pixels, based on the approximation of geodesic distances defined in mani- ...on a (pos- sibly nonlinear) manifold ... Voir le document complet
6
Nonlinear unmixing of hyperspectral images: Models and algorithms
... nonlinearity of the criterion resulting from the underlying nonlinear model { ( ) $ and 2) the constraints that have to be satisfied by the parameter vector ...estimation of the parameters can ... Voir le document complet
15
Robust nonnegative matrix factorization for nonlinear unmixing of hyperspectral images
... introduces a robust linear model to describe hyperspec- tral data arising from the mixture of several pure spectral signa- ...new model not only generalizes the commonly used linear mixing ... 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 ...performing a similarity ... Voir le document complet
5
Nonlinear unmixing of hyperspectral images based on multi-kernel learning
... and post-nonlinear mixing ...the Bayesian algorithm de- rived for generalized bilinear model (BilBay) ...performed using independent data to tune their ... Voir le document complet
5
Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery
... complexity of the posterior distributions for the unknown parameters requires to use appropriate simulation methods such as Markov chain Monte Carlo (MCMC) methods ...(EM) algorithm which allows one ... Voir le document complet
12
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 ...performing a similarity ... Voir le document complet
6
Estimating the number of endmembers in hyperspectral images using the normal compositional model and a hierarchical Bayesian algorithm
... distribution of the estimated abundances. Fig. 5. Posterior distribution of the estimated variance ...matrix of the observed ...versions of the well-known Akaike information criterion (AIC) ... Voir le document complet
11
Nonlinear hyperspectral unmixing using Gaussian processes
... in a hyperspectral ...from a set of nonlinearly mixed pixels, based on the approximation of geodesic distances defined in mani- ...on a (pos- sibly nonlinear) manifold ... Voir le document complet
5
Contributions to unsupervised and nonlinear unmixing of hyperspectral data
... propose a new nonlinear mixing model that allows to incorporate spectral prior regarding the nonlinearities at different ...presents a kernel based nonlinear mixing model ... Voir le document complet
166
A new Bayesian unmixing algorithm for hyperspectral images mitigating endmember variability
... an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember ...by a linear combina- tion of random endmembers to take into ... Voir le document complet
7
Nonlinear unmixing of hyperspectral images using a generalized bilinear model
... studies a generalized bilinear model (GBM) for nonlinear unmixing of hyperspectral images due to multipath ...This model is a generalization not only ... Voir le document complet
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