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Found 6 papers, 0 papers with code
Learning Structures in Earth Observation Data with Gaussian Processes
Gaussian Processes (GPs) has experienced tremendous success in geoscience in general and for bio-geophysical parameter retrieval in the last years.
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method
Finally, the heteroscedastic GP model was successfully applied on airborne hyperspectral data for N mapping.
Spectral band selection for vegetation properties retrieval using Gaussian processes regression
GPR-BAT allows (1) to identify the most informative bands in relating spectral data to a biophysical variable, and (2) to find the least number of bands that preserve optimized accurate predictions.
Emulation as an Accurate Alternative to Interpolation in Sampling Radiative Transfer Codes
Computationally expensive Radiative Transfer Models (RTMs) are widely used} to realistically reproduce the light interaction with the Earth surface and atmosphere.
Gradient-based Automatic Look-Up Table Generator for Atmospheric Radiative Transfer Models
Our results indicate that, when compared to a pseudo-random homogeneous distribution of the LUT nodes, GALGA reduces (1) the LUT size by $\sim$75\% and (2) the maximum interpolation relative errors by 0. 5\% It is concluded that automatic LUT design might benefit from the methodology proposed in GALGA to reduce computation time and interpolation errors.
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes
The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications.
