4 citations · 5 across the 3 of their papers we have counts for
3 papers
Toulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques
Romain Thoreau, Laurent Risser, Véronique Achard +2
Airborne hyperspectral images can be used to map the land cover in large urban areas, thanks to their very high spatial and spectral resolutions on a wide spectral domain. While th…
Physics-informed Variational Autoencoders for Improved Robustness to Environmental Factors of Variation
Romain Thoreau, Laurent Risser, Véronique Achard +2
The combination of machine learning models with physical models is a recent research path to learn robust data representations. In this paper, we introduce pVAE, a variational…
Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability
Charlotte Revel, Yannick Deville, Véronique Achard +1
Blind source separation is a common processing tool to analyse the constitution of pixels of hyperspectral images. Such methods usually suppose that pure pixel spectra (endmembers)…