0000000000144581

AUTHOR

José A. Padrón-hidalgo

showing 3 related works from this author

Kernel Anomalous Change Detection for Remote Sensing Imagery

2020

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 applications for which methodologies are available. This paper introduces a nonlinear extension of a full family of anomalous change detectors. In particular, we focus on algorithms that utilize Gaussian and elliptically contoured (EC) distribution and extend them to their nonlinear counterparts based on the theory of reproducing kernels' Hilbert space. We illustrate the performance of the kernel methods introduced in both pervasive and ACD problems with real and simulated changes in multispectral and hyperspectral imagery w…

FOS: Computer and information sciencesComputer scienceGaussianComputer Vision and Pattern Recognition (cs.CV)Multispectral imageComputer Science - Computer Vision and Pattern Recognition0211 other engineering and technologiesMachine Learning (stat.ML)02 engineering and technologysymbols.namesakeStatistics - Machine LearningElectrical and Electronic Engineering021101 geological & geomatics engineeringbusiness.industryHilbert spaceHyperspectral imagingPattern recognitionNonlinear systemKernel methodKernel (image processing)13. Climate actionsymbolsGeneral Earth and Planetary SciencesArtificial intelligencebusinessChange detection
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Unsupervised Anomaly and Change Detection With Multivariate Gaussianization

2022

Anomaly detection (AD) is a field of intense research in remote sensing (RS) image processing. Identifying low probability events in RS images is a challenging problem given the high dimensionality of the data, especially when no (or little) information about the anomaly is available a priori. While a plenty of methods are available, the vast majority of them do not scale well to large datasets and require the choice of some (very often critical) hyperparameters. Therefore, unsupervised and computationally efficient detection methods become strictly necessary, especially now with the data deluge problem. In this article, we propose an unsupervised method for detecting anomalies and changes …

FOS: Computer and information sciencesComputer Science - Machine LearningMultivariate statisticsComputer sciencebusiness.industryComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionFOS: Physical sciencesImage processingPattern recognitionMultivariate normal distributionComputational Physics (physics.comp-ph)Machine Learning (cs.LG)Methodology (stat.ME)Transformation (function)Robustness (computer science)General Earth and Planetary SciencesAnomaly detectionArtificial intelligenceElectrical and Electronic EngineeringbusinessPhysics - Computational PhysicsStatistics - MethodologyChange detectionCurse of dimensionalityIEEE Transactions on Geoscience and Remote Sensing
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Efficient Kernel Cook's Distance for Remote Sensing Anomalous Change Detection

2021

Detecting anomalous changes in remote sensing images is a challenging problem, where many approaches and techniques have been presented so far. We rely on the standard field of multivariate statistics of diagnostic measures, which are concerned about the characterization of distributions, detection of anomalies, extreme events, and changes. One useful tool to detect multivariate anomalies is the celebrated Cook's distance. Instead of assuming a linear relationship, we present a novel kernelized version of the Cook's distance to address anomalous change detection in remote sensing images. Due to the large computational burden involved in the direct kernelization, and the lack of out-…

Atmospheric ScienceMultivariate statisticsComputer scienceMultispectral image0211 other engineering and technologies02 engineering and technology010501 environmental sciences01 natural sciencesField (computer science)13. Climate actionKernel (statistics)KernelizationLeverage (statistics)Computers in Earth SciencesCook's distanceChange detection021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing
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