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Your search keyword '"Delegido, Jesús"' showing total 128 results
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128 results on '"Delegido, Jesús"'

2. Spectral band selection for vegetation properties retrieval using Gaussian processes regression

Author

Verrelst, Jochem, Rivera, Juan Pablo, Gitelson, Anatoly, Delegido, Jesus, Moreno, José, and Camps-Valls, Gustau

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Applications
Abstract

With current and upcoming imaging spectrometers, automated band analysis techniques are needed to enable efficient identification of most informative bands to facilitate optimized processing of spectral data into estimates of biophysical variables. This paper introduces an automated spectral band analysis tool (BAT) based on Gaussian processes regression (GPR) for the spectral analysis of vegetation properties. The GPR-BAT procedure sequentially backwards removes the least contributing band in the regression model for a given variable until only one band is kept. GPR-BAT is implemented within the framework of the free ARTMO's MLRA (machine learning regression algorithms) toolbox, which is dedicated to the transforming of optical remote sensing images into biophysical products. GPR-BAT allows (1) to identify the most informative bands in relating spectral data to a biophysical variable, and (2) to find the least number of bands that preserve optimized accurate predictions. This study concludes that a wise band selection of hyperspectral data is strictly required for optimal vegetation properties mapping.

Published
2020
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19. Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes.

Author

Caballero, Gabriel, Pezzola, Alejandro, Winschel, Cristina, Sanchez Angonova, Paolo, Casella, Alejandra, Orden, Luciano, Salinero-Delgado, Matías, Reyes-Muñoz, Pablo, Berger, Katja, Delegido, Jesús, and Verrelst, Jochem

Subjects
GAUSSIAN processes, TIME series analysis, OPTICAL radar, WINTER wheat, MULTISENSOR data fusion, STRATOCUMULUS clouds
Abstract

Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical domain background and the all-weather imagery provided by radar systems, we propose a data fusion approach focused on the cross-correlation between radar and optical data streams. To do so, we analyzed several multiple-output Gaussian processes (MOGP) models and their ability to fuse efficiently Sentinel-1 (S1) Radar Vegetation Index (RVI) and Sentinel-2 (S2) vegetation water content (VWC) time series over a dry agri-environment in southern Argentina. MOGP models not only exploit the auto-correlations of S1 and S2 data streams independently but also the inter-channel cross-correlations. The S1 RVI and S2 VWC time series at the selected study sites being the inputs of the MOGP models proved to be closely correlated. Regarding the set of assessed models, the Convolutional Gaussian model (CONV) delivered noteworthy accurate data fusion results over winter wheat croplands belonging to the 2020 and 2021 campaigns ( N R M S E w h e a t 2020 = 16.1%; N R M S E w h e a t 2021 = 10.1%). Posteriorly, we removed S2 observations from the S1 & S2 dataset corresponding to the complete phenological cycles of winter wheat from September to the end of December to simulate the presence of clouds in the scenes and applied the CONV model at the pixel level to reconstruct spatiotemporally-latent VWC maps. After applying the fusion strategy, the phenology of winter wheat was successfully recovered in the absence of optical data. Strong correlations were obtained between S2 VWC and S1 & S2 MOGP VWC reconstructed maps for the assessment dates ( R 2 ¯ w h e a t − 2020 = 0.95, R 2 ¯ w h e a t − 2021 = 0.96). Altogether, the fusion of S1 SAR and S2 optical EO data streams with MOGP offers a powerful innovative approach for cropland trait monitoring over cloudy high-latitude regions. [ABSTRACT FROM AUTHOR]

Published
2023
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27. Validation of Water Quality Monitoring Algorithms for Sentinel-2 and Sentinel-3 in Mediterranean Inland Waters with In Situ Reflectance Data

Author

Sòria-Perpinyà, Xavier, primary, Vicente, Eduardo, additional, Urrego, Patricia, additional, Pereira-Sandoval, Marcela, additional, Tenjo, Carolina, additional, Ruíz-Verdú, Antonio, additional, Delegido, Jesús, additional, Soria, Juan Miguel, additional, Peña, Ramón, additional, and Moreno, José, additional

Published
2021
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29. Assessment of Multi-Date Sentinel-1 Polarizations and GLCM Texture Features Capacity for Onion and Sunflower Classification in an Irrigated Valley: An Object Level Approach

Author

Caballero, Gabriel Rodrigo, primary, Platzeck, Gabriel, additional, Pezzola, Alejandro, additional, Casella, Alejandra, additional, Winschel, Cristina, additional, Silva, Samanta Soledad, additional, Ludueña, Emilia, additional, Pasqualotto, Nieves, additional, and Delegido, Jesús, additional

Published
2020
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