Listar por autor UPNA "Pérez-Goya, Unai"
Mostrando ítems 1-12 de 12
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Detecting change-points in the time series of surfaces occupied by pre-defined NDVI categories in continental Spain from 1981 to 2015
The free access to satellite images since more than 40 years ago has provoked a rapid increase of multitemporal derived information of remote sensing data that should be summarized and analyzed for future inferences. ... -
Hierarchical spatio-temporal change-point detection
Detecting change-points in multivariate settings is usually carried out by analyzing all marginals either independently, via univariate methods, or jointly, through multivariate approaches. The former discards any inherent ... -
Improving the quality of satellite imagery based on ground-truth data from rain gauge stations
Multitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated ... -
Interpolation of the mean anomalies for cloud filling in land surface temperature and normalized difference vegetation index
When monitoring time series of remote sensing data, it is advisable to fill gaps, i.e., missing or distorted data, caused by atmospheric effects or technical failures. In this paper, a new method for filling these gaps ... -
An introduction to the spatio-temporal analysis of satellite remote sensing data for geostatisticians
Satellite remote sensing data have become available in meteorology, agriculture, forestry, geology, regional planning, hydrology or natural environment sciences since several decades ago, because satellites provide routinely ... -
Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences
Temporal sequences of satellite images constitute a highly valuable and abundant resource for analyzing regions of interest. However, the automatic acquisition of knowledge on a large scale is a challenging task due to ... -
Logistic regression versus XGBoost for detecting burned areas using satellite images
Classical statistical methods prove advantageous for small datasets, whereas machine learning algorithms can excel with larger datasets. Our paper challenges this conventional wisdom by addressing a highly significant ... -
Machine learning procedures for daily interpolation of rainfall in Navarre (Spain)
(Springer, 2023) Capítulo de libro / Liburuen kapituluaKriging is by far the most well known and widely used statistical method for interpolating data in spatial random fields. The main reason is that it provides the best linear unbiased predictor and it is an exact interpolator ... -
Software tools and statistical methods for downloading, processing, and analysing satellite images
El principal objetivo de esta tesis es la introducción y desarrollo de métodos estadísticos en imágenes satelitales para mejorar el procesamiento, suavizado, predicción, e inferencia de los datos de teledetección. Este ... -
Stochastic spatio-temporal models for analysing NDVI distribution of GIMMS NDVI3g images
The normalized difference vegetation index (NDVI) is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by ... -
Unpaired spatio-temporal fusion of image patches (USTFIP) from cloud covered images
Spatio-temporal image fusion aims to increase the frequency and resolution of multispectral satellite sensor images in a cost-effective manner. However, practical constraints on input data requirements and computational ... -
Using RGISTools to estimate water levels in reservoirs and lakes
The combination of freely accessible satellite imagery from multiple programs improves the spatio-temporal coverage of remote sensing data, but it exhibits barriers regarding the variety of web services, file formats, and ...