Výsledky vyhledávání - "José Estévez"

Akademický článek

Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery

Autor: Ana B. Pascual-Venteo, Jose L. Garcia, Katja Berger, José Estévez, Jorge Vicent, Adrián Pérez-Suay, Shari Van Wittenberghe, Jochem Verrelst

Publikováno v: Remote Sensing, Vol 16, Iss 7, p 1211 (2024)

The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such a

Externí odkaz: https://doaj.org/article/4cee256037a248249168ae2ccc0df845

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Akademický článek

In-Depth Evaluation of Saliency Maps for Interpreting Convolutional Neural Network Decisions in the Diagnosis of Glaucoma Based on Fundus Imaging

Autor: Jose Sigut, Francisco Fumero, José Estévez, Silvia Alayón, Tinguaro Díaz-Alemán

Publikováno v: Sensors, Vol 24, Iss 1, p 239 (2023)

Glaucoma, a leading cause of blindness, damages the optic nerve, making early diagnosis challenging due to no initial symptoms. Fundus eye images taken with a non-mydriatic retinograph help diagnose glaucoma by revealing structural changes, including

Externí odkaz: https://doaj.org/article/c5b9622300234219a1d31f540b380f1f

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Akademický článek

Interpretable surrogate models to approximate the predictions of convolutional neural networks in glaucoma diagnosis

Autor: Jose Sigut, Francisco Fumero, Rafael Arnay, José Estévez, Tinguaro Díaz-Alemán

Publikováno v: Machine Learning: Science and Technology, Vol 4, Iss 4, p 045024 (2023)

Deep learning systems, especially in critical fields like medicine, suffer from a significant drawback, their black box nature, which lacks mechanisms for explaining or interpreting their decisions. In this regard, our research aims to evaluate the u

Externí odkaz: https://doaj.org/article/e1abd2f2f9a1422196949c89883fe4b4

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Akademický článek

Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine

Autor: Pablo Reyes-Muñoz, Luca Pipia, Matías Salinero-Delgado, Santiago Belda, Katja Berger, José Estévez, Miguel Morata, Juan Pablo Rivera-Caicedo, Jochem Verrelst

Publikováno v: Remote Sensing, Vol 14, Iss 6, p 1347 (2022)

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3

Externí odkaz: https://doaj.org/article/e82611827de54fedadefc0953b2af57b

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Akademický článek

Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression

Autor: Matías Salinero-Delgado, José Estévez, Luca Pipia, Santiago Belda, Katja Berger, Vanessa Paredes Gómez, Jochem Verrelst

Publikováno v: Remote Sensing, Vol 14, Iss 1, p 146 (2021)

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamic

Externí odkaz: https://doaj.org/article/6274f420754846579a127fba8ece6afd

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Akademický článek

Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow

Autor: José Estévez, Katja Berger, Jorge Vicent, Juan Pablo Rivera-Caicedo, Matthias Wocher, Jochem Verrelst

Publikováno v: Remote Sensing, Vol 13, Iss 8, p 1589 (2021)

In support of cropland monitoring, operational Copernicus Sentinel-2 (S2) data became available globally and can be explored for the retrieval of important crop traits. Based on a hybrid workflow, retrieval models for six essential biochemical and bi

Externí odkaz: https://doaj.org/article/aeea5e08d48d45a0831b608182db1642

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Akademický článek

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Systematic Application of Saliency Maps to Explain the Decisions of Convolutional Neural Networks for Glaucoma Diagnosis Based on Disc and Cup Geometry

Autor: Francisco Fumero, José Sigut, José Estévez, Tinguaro Diaz-Aleman

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b753fe153b3eb528ffa531a60987a95a
https://doi.org/10.2139/ssrn.4327677

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Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow

Autor: Jorge Vicent, José Estévez, Katja Berger, Juan Pablo Rivera-Caicedo, Jochem Verrelst, Matthias Wocher

Publikováno v: Remote Sensing
Remote Sensing; Volume 13; Issue 8; Pages: 1589
Remote Sensing, Vol 13, Iss 1589, p 1589 (2021)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f6ddb37306c31985432285ea56ee246
https://pubmed.ncbi.nlm.nih.gov/36082340

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Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Autor: José Estévez, Matías Salinero-Delgado, Katja Berger, Luca Pipia, Juan Pablo Rivera-Caicedo, Matthias Wocher, Pablo Reyes-Muñoz, Giulia Tagliabue, Mirco Boschetti, Jochem Verrelst

Publikováno v: José Estévez, Matías Salinero-Delgado, Katja Berger, Luca Pipia, Juan Pablo Rivera-Caicedo, Matthias Wocher, Pablo Reyes-Muñoz, Giulia Tagliabue, Mirco Boschetti, Jochem Verrelst (2022). Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data. Remote Sensing of Environment, 273, 112958.
Remote Sensing of Environment

The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval mod

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::824150b7fb5e755b4f72a1c0da194ded
https://pubmed.ncbi.nlm.nih.gov/36081832

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