
On the relation between statistical learning and perceptual distances
It has been demonstrated many times that the behavior of the human visua...
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Learning Structures in Earth Observation Data with Gaussian Processes
Gaussian Processes (GPs) has experienced tremendous success in geoscienc...
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Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models
Gaussian Processes (GPs) are a class of kernel methods that have shown t...
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Consistent regression of biophysical parameters with kernel methods
This paper introduces a novel statistical regression framework that allo...
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Kernel Anomalous Change Detection for Remote Sensing Imagery
Anomalous change detection (ACD) is an important problem in remote sensi...
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Spatial noiseaware temperature retrieval from infrared sounder data
In this paper we present a combined strategy for the retrieval of atmosp...
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Randomized kernels for large scale Earth observation applications
Dealing with land cover classification of the new image sources has also...
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PhysicsAware Gaussian Processes in Remote Sensing
Earth observation from satellite sensory data poses challenging problems...
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Information Theory in Density Destructors
Density destructors are differentiable and invertible transforms that ma...
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Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis
Information theory is an excellent framework for analyzing Earth system ...
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Information Theory Measures via Multidimensional Gaussianization
Information theory is an outstanding framework to measure uncertainty, d...
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Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences
Kernel methods are powerful machine learning techniques which implement ...
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CrossSensor Adversarial Domain Adaptation of Landsat8 and ProbaV images for Cloud Detection
The number of Earth observation satellites carrying optical sensors with...
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Accounting for Input Noise in Gaussian Process Parameter Retrieval
Gaussian processes (GPs) are a class of Kernel methods that have shown t...
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PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance
Traditionally, the vision community has devised algorithms to estimate t...
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Enforcing Perceptual Consistency on Generative Adversarial Networks by Using the Normalised Laplacian Pyramid Distance
In recent years there has been a growing interest in image generation th...
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Fair Kernel Learning
New social and economic activities massively exploit big data and machin...
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EigenDistortions of Hierarchical Representations
We develop a method for comparing hierarchical image representations in ...
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Perceptually Optimized Image Rendering
We develop a framework for rendering photographic images, taking into ac...
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Endtoend Optimized Image Compression
We describe an image compression method, consisting of a nonlinear analy...
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Sequential Principal Curves Analysis
This work includes all the technical details of the Sequential Principal...
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Optimized Kernel Entropy Components
This work addresses two main issues of the standard Kernel Entropy Compo...
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Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis
Mechanisms of human color vision are characterized by two phenomenologic...
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Iterative Gaussianization: from ICA to Random Rotations
Most signal processing problems involve the challenging task of multidim...
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Principal Polynomial Analysis
This paper presents a new framework for manifold learning based on a seq...
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Image Denoising with Kernels based on Natural Image Relations
A successful class of image denoising methods is based on Bayesian appro...
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Dimensionality Reduction via Regression in Hyperspectral Imagery
This paper introduces a new unsupervised method for dimensionality reduc...
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