Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Giuseppina De Felice Proia"'
Autor:
Paolo Fazzini, Giuseppina De Felice Proia, Maria Adamo, Palma Blonda, Francesco Petracchini, Luigi Forte, Cristina Tarantino
Publikováno v:
Remote Sensing, Vol 13, Iss 12, p 2276 (2021)
The present study focuses on the use of Convolutional Neural Networks (CNN or ConvNet) to classify a multi-seasonal dataset of Sentinel-2 images to discriminate four grassland habitats in the “Murgia Alta” protected site. To this end, we compared
Externí odkaz:
https://doaj.org/article/1deedf1ae3f641e186aef6947f87a274
Autor:
Giuseppina De Felice Proia, Marco Restano, Davide Comite, Maria Paola Clarizia, Jerome Benveniste, Nazzareno Pierdicca, Leila Guerriero
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-11
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::715be8a5ed069eeb66ead3b7f0f4f27b
https://doi.org/10.1109/tgrs.2022.3210720
https://doi.org/10.1109/tgrs.2022.3210720
Autor:
Giuseppina De Felice Proia, Marco Restano, Davide Comite, Maria Paola Clarizia, Jerome Benveniste, Nazzareno Pierdicca, Leila Guerriero
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-10
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ab0d331e1eaee7ae7d8fc04067a7cae
https://doi.org/10.1109/tgrs.2022.3210722
https://doi.org/10.1109/tgrs.2022.3210722
Publikováno v:
Climate; Volume 10; Issue 12; Pages: 193
The Sahel is one of the regions with the highest rates of food insecurity in the world. Understanding the driving factors of agricultural productivity is, therefore, essential for increasing crop yields whilst adapting to a future that will be increa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c42b9a0fcfa7817395cb1dea9defe157
https://doi.org/10.3390/cli10120193
https://doi.org/10.3390/cli10120193
Autor:
Marco Restano, Davide Comite, Jérôme Benveniste, Giuseppina De Felice-Proia, Nazzareno Pierdicca, Maria Paola Clarizia, Leila Guerriero
Publikováno v:
Microwave Remote Sensing: Data Processing and Applications.
Sentinel-3 is a multi-instrument mission designed to measure sea-surface topography, sea- and land-surface temperature, ocean color and land color with high resolution and accuracy. The S3 mission is based on a constellation of two (3A and 3B) polar-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2557851a3f9fc5a3a5b8b03fe7298247
https://doi.org/10.1117/12.2600230
https://doi.org/10.1117/12.2600230
Autor:
Leila Guerriero, Marco Restano, Cristina Vittucci, Nazzareno Pierdicca, Maria Paola Clarizia, Daniel Pascual, Giuseppina De Felice-Proia, Davide Comite, Jérôme Benveniste
The ALtimetry for BIOMass (ALBIOM) project is an ESA-funded Permanent Open Call Project that aims to retrieve forest biomass using Copernicus Sentinel-3 (S3) altimeter data. The overall goal of ALBIOM is to estimate biomass with sufficient accuracy t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a2bdafe7de20dc43f004715a6ba3f648
https://doi.org/10.5194/egusphere-egu21-12180
https://doi.org/10.5194/egusphere-egu21-12180
Autor:
Jennifer Reynolds, Maria Paola Clarizia, Marco Restano, Davide Comite, Cristina Vittucci, Alireza Taravat, Leila Guerriero, Jérôme Benveniste, Nazzareno Pierdicca, Giuseppina De Felice-Proia
The ALtimetry for BIOMass project (ALBIOM) is ESA-funded Permanent Open Call Project that proposes to derive forest biomass using Copernicus Sentinel-3 (S3) SAR altimeter data. The project targets the need to improve our current global observations o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0427cfc37cca77069d78b28e0be702e0
https://doi.org/10.5194/egusphere-egu2020-20303
https://doi.org/10.5194/egusphere-egu2020-20303
Autor:
Cristina Tarantino, Luigi Forte, Maria Adamo, Paolo Fazzini, Francesco Petracchini, Giuseppina De Felice Proia, Palma Blonda
Publikováno v:
Remote Sensing; Volume 13; Issue 12; Pages: 2276
Remote Sensing, Vol 13, Iss 2276, p 2276 (2021)
Remote Sensing, Vol 13, Iss 2276, p 2276 (2021)
The present study focuses on the use of Convolutional Neural Networks (CNN or ConvNet) to classify a multi-seasonal dataset of Sentinel-2 images to discriminate four grassland habitats in the “Murgia Alta” protected site. To this end, we compared
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::936923e1131aecdc97e4dbe5a7880aa7