1.3k citations · 2.4k across the 52 of their papers we have counts for
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Learning Structures in Earth Observation Data with Gaussian Processes
Fernando Mateo, Jordi Munoz-Mari, Valero Laparra +2
Gaussian Processes (GPs) has experienced tremendous success in geoscience in general and for bio-geophysical parameter retrieval in the last years. GPs constitute a solid Bayesian…
Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval
David Malmgren-Hansen, Allan Aasbjerg Nielsen, Valero Laparra +1
The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP). Retrieving accur…
Spatial noise-aware temperature retrieval from infrared sounder data
David Malmgren-Hansen, Valero Laparra, Allan Aasbjerg Nielsen +1
In this paper we present a combined strategy for the retrieval of atmospheric profiles from infrared sounders. The approach considers the spatial information and a noise-dependent…
Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models
Juan Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls
Gaussian Processes (GPs) are a class of kernel methods that have shown to be very useful in geoscience applications. They are widely used because they are simple, flexible and prov…
Consistent regression of biophysical parameters with kernel methods
Emiliano Díaz, Adrián Pérez-Suay, Valero Laparra +1
This paper introduces a novel statistical regression framework that allows the incorporation of consistency constraints. A linear and nonlinear (kernel-based) formulation are intro…
Kernel Anomalous Change Detection for Remote Sensing Imagery
José A. Padrón-Hidalgo, Valero Laparra, Nathan Longbotham +1
Anomalous change detection (ACD) is an important problem in remote sensing image processing. Detecting not only pervasive but also anomalous or extreme changes has many application…