55 citations · 136 across the 8 of their papers we have counts for
8 papers
Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images
Adrien Lagrange, Mathieu Fauvel, Stéphane May +1
Hyperspectral unmixing aims at identifying a set of elementary spectra and the corresponding mixture coefficients for each pixel of an image. As the elementary spectra correspond t…
Matrix Cofactorization for Joint Representation Learning and Supervised Classification -- Application to Hyperspectral Image Analysis
Adrien Lagrange, Mathieu Fauvel, Stéphane May +2
Supervised classification and representation learning are two widely used classes of methods to analyze multivariate images. Although complementary, these methods have been scarcel…
Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity
Tatsumi Uezato, Mathieu Fauvel, Nicolas Dobigeon
Spectral variability is one of the major issue when conducting hyperspectral unmixing. Within a given image composed of some elementary materials (herein referred to as endmember c…
Hyperspectral image unmixing with LiDAR data-aided spatial regularization
Tatsumi Uezato, Mathieu Fauvel, Nicolas Dobigeon
Spectral unmixing methods incorporating spatial regularizations have demonstrated increasing interest. Although spatial regularizers which promote smoothness of the abundance maps…
Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning
Adrien Lagrange, Mathieu Fauvel, Stéphane May +1
Within a supervised classification framework, labeled data are used to learn classifier parameters. Prior to that, it is generally required to perform dimensionality reduction via…
Fast forward feature selection for the nonlinear classification of hyperspectral images
Mathieu Fauvel, Clement Dechesne, Anthony Zullo +1
A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images.…
Parsimonious Mahalanobis Kernel for the Classification of High Dimensional Data
M. Fauvel, A. Villa, J. Chanussot +1
The classification of high dimensional data with kernel methods is considered in this article. Exploit- ing the emptiness property of high dimensional spaces, a kernel based on the…
Kernel discriminant analysis and clustering with parsimonious Gaussian process models
Charles Bouveyron, Stéphane Girard, Mathieu Fauvel
This work presents a family of parsimonious Gaussian process models which allow to build, from a finite sample, a model-based classifier in an infinite dimensional space. The propo…