Search Results for author:
Found 2 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
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.
p$^3$VAE: a physics-integrated generative model. Application to the pixel-wise classification of airborne hyperspectral images
The combination of machine learning models with physical models is a recent research path to learn robust data representations.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
