Search Results for author: Véronique Achard

Found 3 papers, 2 papers with code

Toulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques

2 code implementations15 Nov 2023 Romain Thoreau, Laurent Risser, Véronique Achard, Béatrice Berthelot, Xavier Briottet

While the spectral dimension of hyperspectral images is highly informative of the chemical composition of the land surface, the use of state-of-the-art machine learning algorithms to map the land cover has been dramatically limited by the availability of training data.

Representation Learning Self-Supervised Learning

Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability

no code implementations24 Feb 2017 Charlotte Revel, Yannick Deville, Véronique Achard, Xavier Briottet

Unconstrained Pixel-by-pixel NMF (UP-NMF) is a new blind source separation method based on the assumption of a linear mixing model, which can deal with intra-class variability.

blind source separation Hyperspectral Unmixing

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