4 citations · 5 across the 4 of their papers we have counts for
5 papers
Land Surface Temperature Super-Resolution with a Scale-Invariance-Free Neural Approach: Application to MODIS
Romuald Ait-Bachir, Carlos Granero-Belinchon, Aurélie Michel +3
Due to the trade-off between the temporal and spatial resolution of thermal spaceborne sensors, super-resolution methods have been developed to provide fine-scale Land SurfaceTempe…
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)…
Hyperspectral pansharpening: a review
Laetitia Loncan, Luis B. Almeida, José M. Bioucas-Dias +12
Pansharpening aims at fusing a panchromatic image with a multispectral one, to generate an image with the high spatial resolution of the former and the high spectral resolution of…