1.3k citations · 2.4k across the 52 of their papers we have counts for
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Unsupervised Anomaly and Change Detection with Multivariate Gaussianization
José A. Padrón-Hidalgo, Valero Laparra, Gustau Camps-Valls
Anomaly detection is a field of intense research. Identifying low probability events in data/images is a challenging problem given the high-dimensionality of the data, especially w…
Inference over radiative transfer models using variational and expectation maximization methods
Daniel Heestermans Svendsen, Daniel Hernández-Lobato, Luca Martino +3
Earth observation from satellites offers the possibility to monitor our planet with unprecedented accuracy. Radiative transfer models (RTMs) encode the energy transfer through the…
Randomized kernels for large scale Earth observation applications
Adrián Pérez-Suay, Julia Amorós-López, Luis Gómez-Chova +3
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. In order to cope with…
Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences
J. Emmanuel Johnson, Valero Laparra, Adrián Pérez-Suay +2
Kernel methods are powerful machine learning techniques which implement generic non-linear functions to solve complex tasks in a simple way. They Have a solid mathematical backgrou…
Accounting for Input Noise in Gaussian Process Parameter Retrieval
J. 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 and remote sensing applications for parameter retrieval, model inversion, and…
PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance
Alexander Hepburn, Valero Laparra, Jesús Malo +2
Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations. Inspiration was us…
Density Modeling of Images using a Generalized Normalization Transformation
Johannes Ballé, Valero Laparra, Eero P. Simoncelli
We introduce a parametric nonlinear transformation that is well-suited for Gaussianizing data from natural images. The data are linearly transformed, and each component is then nor…