Search results for "Gaussian processes"
showing 10 items of 22 documents
Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring
2020
Image processing entered the era of artificial intelligence, and machine learning algorithms emerged as attractive alternatives for time series data processing. Satellite image time series processing enables crop phenology monitoring, such as the calculation of start and end of season. Among the promising algorithms, Gaussian process regression (GPR) proved to be a competitive time series gap-filling algorithm with the advantage of, as developed within a Bayesian framework, providing associated uncertainty estimates. Nevertheless, the processing of time series images becomes computationally inefficient in its standard per-pixel usage, mainly for GPR training rather than the fitting step. To…
An Interactive Framework for Offline Data-Driven Multiobjective Optimization
2020
We propose a framework for solving offline data-driven multiobjective optimization problems in an interactive manner. No new data becomes available when solving offline problems. We fit surrogate models to the data to enable optimization, which introduces uncertainty. The framework incorporates preference information from a decision maker in two aspects to direct the solution process. Firstly, the decision maker can guide the optimization by providing preferences for objectives. Secondly, the framework features a novel technique for the decision maker to also express preferences related to maximum acceptable uncertainty in the solutions as preferred ranges of uncertainty. In this way, the d…
Integrating Physics Modelling with Machine Learning for Remote Sensing
2020
L’observació de la Terra a partir de les dades proporcionades per sensors abord de satèl·lits, així com les proporcionades per models de transferència radiativa o climàtics, juntament amb les mesures in situ proporcionen una manera sense precedents de monitorar el nostre planeta amb millors resolucions espacials i temporals. La riquesa, quantitat i diversitat de les dades adquirides i posades a disposició també augmenta molt ràpidament. Aquestes dades ens permeten predir el rendiment dels cultius, fer un seguiment del canvi d’ús del sòl com ara la desforestació, supervisar i respondre als desastres naturals, i predir i mitigar el canvi climàtic. Per tal de fer front a tots aquests reptes, l…
Color degradation mapping of rock art paintings using microfading spectrometry
2021
[EN] Rock art documentation is a complex task that should be carried out in a complete, rigorous and exhaustive way, in order to take particular actions that allow stakeholders to preserve the archaeological sites under constant deterioration. The pigments used in prehistoric paintings present high light sensitivity and rigorous scientific color degradation mapping is not usually undertaken in overall archaeological sites. Microfading spectrometry is a suitable technique for determining the light-stability of pigments found in rock art paintings in a non-destructive way. Spectral data can be transformed into colorimetric information following the recommendations published by the Commission …
Multi-fidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models
2023
This repository contains several datasets of spectral atmospheric transfer functions (i.e. path radiance, transmittances, spherical albedo) simulated with MODTRAN6 atmospheric radiative transfer model. The simulations are stored in hdf5 files using the Atmospheric Look-up table Generator (ALG) toolbox (https://doi.org/10.5194/gmd-13-1945-2020). Each dataset has an associated .xml file that includes the configuration of ALG/MODTRAN6 executions. All datasets include the input atmospheric/geometric variables that are summarized in the following table. Each dataset file has a random distribution (based on latin hypercube sampling) these input variables with varying number of points (e.g. train5…
A new Framework for the Spectral Information Decomposition of Multivariate Gaussian Processes
2021
: Different information-theoretic measures are available in the literature for the study of pairwise and higher-order interactions in multivariate dynamical systems. While these measures operate in the time domain, several physiological and non-physiological systems exhibit a rich oscillatory content that is typically analyzed in the frequency domain through spectral and cross-spectral approaches. For Gaussian systems, the relation between information and spectral measures has been established considering coupling and causality measures, but not for higher-order interactions. To fill this gap, in this work we introduce an information-theoretic framework in the frequency domain to quantify t…
Seminararbeiten von Semester 71/72, 72, 72/73
1801
Il Filtro Integrale Auto-Regressivo Continuo (I-ARC) per l’Analisi di Strutture Esposte al Vento
2010
In questo studio viene proposto un metodo per la rappresentazione di processi aleatori Gaussiani e stazionari, utile a modellare la turbolenza della velocità del vento, introducendo la versione integrale del modello auto-regressivo discreto già proposto in precedenza. La rappresentazione di un processo aleatorio di assegnata funzione di correlazione viene condotta integrando un’equazione integro-differenziale in cui viene coinvolto un nucleo, che rappresenta la memoria del processo, in presenza di un rumore bianco Gaussiano. La soluzione dell’equazione rappresenta un campione del processo aleatorio della turbolenza della velocità del vento. E’ stato mostrato che il modello I-ARC fornisce, n…
A perspective on Gaussian processes for Earth observation
2019
Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and Gaussian processes (GPs) in particular has attained outstanding results in the estimation of bio-geo-physical variables from the acquired images at local and global scales in a time-resolved manner. GPs provide not only accurate estimates but also principled uncertainty estimates for the predictions, can easily accommodate multimodal data coming from different sensors and from multitemporal acquisitions, allow the introduction of physical knowledge, and a formal treatment of uncertainty quantification and error pr…
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method
2020
Abstract Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-base…