N
Nicolas Dobigeon
Researcher at University of Toulouse
Publications - 267
Citations - 13074
Nicolas Dobigeon is an academic researcher from University of Toulouse. The author has contributed to research in topics: Hyperspectral imaging & Gibbs sampling. The author has an hindex of 45, co-authored 253 publications receiving 11241 citations. Previous affiliations of Nicolas Dobigeon include University of Michigan & Institut Universitaire de France.
Papers
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Proceedings ArticleDOI
Sampling from a multivariate Gaussian distribution truncated on a simplex: a review
TL;DR: This paper reviews recent Monte Carlo methods for sampling from multivariate Gaussian distributions restricted to the standard simplex and describes and analyzes two Hamiltonian Monte Carlo Methods.
Journal ArticleDOI
Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization
TL;DR: The results show that the proposed framework can provide better abundance estimates and, more specifically, can significantly improve the abundance estimates for the pixels affected by shadows.
Journal ArticleDOI
Semi-Blind Sparse Image Reconstruction With Application to MRFM
TL;DR: A solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known, which adopts a Bayesian Metropolis-within-Gibbs sampling framework.
Proceedings ArticleDOI
Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery
TL;DR: This paper studies a hierarchical Bayesian model for nonlinear hyperspectral image unmixing that assumes that the pixel reflectances are polynomial functions of linear mixtures of pure spectral components contaminated by an additive white Gaussian noise.
Proceedings ArticleDOI
Robust nonnegative matrix factorization for nonlinear unmixing of hyperspectral images
Nicolas Dobigeon,Cédric Févotte +1 more
TL;DR: A robust linear model to describe hyperspectral data arising from the mixture of several pure spectral signatures is introduced, which allows for possible nonlinear effects to be handled and competes with state-of-the-art linear and nonlinear unmixing methods.