138 citations · 213 across the 16 of their papers we have counts for
17 papers
PLS-based approach for fair representation learning
Elena M. De-Diego, Adrián Perez-Suay, Paula Gordaliza +1
We revisit the problem of fair representation learning by proposing Fair Partial Least Squares (PLS) components. PLS is widely used in statistics to efficiently reduce the dimensio…
Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation
Anna Mateo-Sanchis, Maria Piles, Jordi Muñoz-Marí +3
Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation remote sensing data provides…
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference
Anna Mateo-Sanchis, Jordi Muñoz-Marí, Adrián Pérez-Suay +1
This paper introduces warped Gaussian processes (WGP) regression in remote sensing applications. WGP models output observations as a parametric nonlinear transformation of a GP. Th…
A deep network approach to multitemporal cloud detection
Devis Tuia, Benjamin Kellenberger, Adrian Pérez-Suay +1
We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation (MSG) satellite.…
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…
Randomized RX for target detection
Fatih Nar, Adrián Pérez-Suay, José Antonio Padrón +1
This work tackles the target detection problem through the well-known global RX method. The RX method models the clutter as a multivariate Gaussian distribution, and has been exten…
Nonlinear Cook distance for Anomalous Change Detection
José A. Padrón Hidalgo, Adrián Pérez-Suay, Fatih Nar +1
In this work we propose a method to find anomalous changes in remote sensing images based on the chronochrome approach. A regressor between images is used to discover the most {\em…
Pattern Recognition Scheme for Large-Scale Cloud Detection over Landmarks
Adrián Pérez-Suay, Julia Amorós-López, Luis Gómez-Chova +3
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 geometr…
Causal Inference in Geosciences with Kernel Sensitivity Maps
Adrián Pérez-Suay, Gustau Camps-Valls
Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's Science. In remote sensing and geosciences this is…
Nonlinear Distribution Regression for Remote Sensing Applications
Jose E. Adsuara, Adrián Pérez-Suay, Jordi Muñoz-Marí +3
In many remote sensing applications one wants to estimate variables or parameters of interest from observations. When the target variable is available at a resolution that matches…
Efficient Nonlinear RX Anomaly Detectors
José A. Padrón Hidalgo, Adrián Pérez-Suay, Fatih Nar +1
Current anomaly detection algorithms are typically challenged by either accuracy or efficiency. More accurate nonlinear detectors are typically slow and not scalable. In this lette…
Causal Inference in Geoscience and Remote Sensing from Observational Data
Adrián Pérez-Suay, Gustau Camps-Valls
Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's \blue{science}. In remote sensing and geosciences…
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 Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness
Zhu Li, Adrian Perez-Suay, Gustau Camps-Valls +1
Current adoption of machine learning in industrial, societal and economical activities has raised concerns about the fairness, equity and ethics of automated decisions. Predictive…
Fair Kernel Learning
Adrián Pérez-Suay, Valero Laparra, Gonzalo Mateo-García +3
New social and economic activities massively exploit big data and machine learning algorithms to do inference on people's lives. Applications include automatic curricula evaluation…
Remote Sensing Image Classification with Large Scale Gaussian Processes
Pablo Morales-Alvarez, Adrian Perez-Suay, Rafael Molina +1
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large…
Sensitivity Maps of the Hilbert-Schmidt Independence Criterion
Adrián Pérez-Suay, Gustau Camps-Valls
Kernel dependence measures yield accurate estimates of nonlinear relations between random variables, and they are also endorsed with solid theoretical properties and convergence ra…