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Found 14 papers, 0 papers with code
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions
We specifically propose the use of a class of GP convolution models called latent force models (LFMs) for EO time series modelling, analysis and understanding.
Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation
Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food.
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference
This paper introduces warped Gaussian processes (WGP) regression in remote sensing applications.
Cloud detection machine learning algorithms for PROBA-V
This paper presents the development and implementation of a cloud detection algorithm for Proba-V.
Pattern Recognition Scheme for Large-Scale Cloud Detection over Landmarks
Landmark recognition and matching is a critical step in many Image Navigation and Registration (INR) models for geostationary satellite services, as well as to maintain the geometric quality assessment (GQA) in the instrument data processing chain of Earth observation satellites.
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.
Physics-Aware Gaussian Processes in Remote Sensing
However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only.
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval
Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes.
Randomized kernels for large scale Earth observation applications
Dealing with land cover classification of the new image sources has also turned to be a complex problem requiring large amount of memory and processing time.
Nonlinear Distribution Regression for Remote Sensing Applications
When the target variable is available at a resolution that matches the remote sensing observations, standard algorithms such as neural networks, random forests or Gaussian processes are readily available to relate the two.
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.
Living in the Physics and Machine Learning Interplay for Earth Observation
Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem.
Fair Kernel Learning
It has been shown that not including sensitive features that bias fairness, such as gender or race, is not enough to mitigate the discrimination when other related features are included.
A Unified SVM Framework for Signal Estimation
On the one hand, the signal model equation is written in reproducing kernel Hilbert spaces (RKHS) using the well-known RKHS Signal Model formulation, and Mercer's kernels are readily used in SVM non-linear algorithms.
