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12 results on '"Caporusso, Giacomo"'

1. Change Detection Analysis Using Sentinel-1 Satellite Data with SNAP and GEE Regarding Oil Spill in Venezuela

Author

Caporusso, Giacomo, Gallo, Cristian, Tarantino, Eufemia, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Rocha, Ana Maria A. C., editor, and Garau, Chiara, editor

Published
2022
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2. Use of the Sentinel-1 Satellite Data in the SNAP Platform and the WebGNOME Simulation Model for Change Detection Analyses on the Persian Gulf Oil Spill

Author

Caporusso, Giacomo, Dell’Olio, Marino, Tarantino, Eufemia, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Rocha, Ana Maria A. C., editor, and Garau, Chiara, editor

Published
2022
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3. Modeling Land Cover Impact on Albedo Changes in Google Earth Engine Environment

Author

Capolupo, Alessandra, Monterisi, Cristina, Sonnessa, Alberico, Caporusso, Giacomo, Tarantino, Eufemia, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Garau, Chiara, editor, Blečić, Ivan, editor, Taniar, David, editor, Apduhan, Bernady O., editor, Rocha, Ana Maria A. C., editor, Tarantino, Eufemia, editor, and Torre, Carmelo Maria, editor

Published
2021
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4. Extracting Land Cover Data Using GEE: A Review of the Classification Indices

Author

Capolupo, Alessandra, Monterisi, Cristina, Caporusso, Giacomo, Tarantino, Eufemia, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Garau, Chiara, editor, Blečić, Ivan, editor, Taniar, David, editor, Apduhan, Bernady O., editor, Rocha, Ana Maria A. C., editor, Tarantino, Eufemia, editor, Torre, Carmelo Maria, editor, and Karaca, Yeliz, editor

Published
2020
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9. High-Resolution Flood Monitoring Based on Advanced Statistical Modeling of Sentinel-1 Multi-Temporal Stacks.

Author

Colacicco, Rosa, Refice, Alberto, Nutricato, Raffaele, Bovenga, Fabio, Caporusso, Giacomo, D'Addabbo, Annarita, La Salandra, Marco, Lovergine, Francesco Paolo, Nitti, Davide Oscar, and Capolongo, Domenico

Subjects
FLOOD warning systems, STATISTICAL models, DIGITAL elevation models, GAUSSIAN processes, COASTAL plains, PERIODIC functions, FLOODS, FLOOD damage prevention
Abstract

High-resolution flood monitoring can be achieved relying on multi-temporal analysis of remote sensing SAR data, through the implementation of semi-automated systems. Exploiting a Bayesian inference framework, conditioned probabilities can be estimated for the presence of floodwater at each image location and each acquisition date. We developed a procedure for efficient monitoring of floodwaters from SAR data cubes, which adopts a statistical modelling framework for SAR backscatter time series over normally unflooded areas based on Gaussian processes (GPs), in order to highlight flood events as outliers, causing abrupt variations in the trends. We found that non-parametric time series modelling improves the performances of Bayesian probabilistic inference with respect to state-of-the-art methodologies using, e.g., parametric fits based on periodic functions, by both reducing false detections and increasing true positives. Our approach also exploits ancillary data derived from a digital elevation model, including slopes, normalized heights above nearest drainage (HAND), and SAR imaging parameters such as shadow and layover conditions. It is here tested over an area that includes the so-called Metaponto Coastal Plain (MCP), in the Basilicata region (southern Italy), which is recurrently subject to floods. We illustrate the ability of our system to detect known (although not ground-truthed) and smaller, undocumented inundation events over large areas, and propose some consideration about its prospective use for contexts affected by similar events, over various land cover scenarios and climatic settings. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Investigating the feasibility of using Sentinel-1 data to determine the damage state of Ukrainian territory = Indagare sulla fattibilità dell'utilizzo dei dati Sentinel-1 per determinare lo stato di danneggiamento del territorio Ucraino

Author

Caporusso, Giacomo

Subjects
Ukraine, war damage, sentinel-1, UNOSAT, polarimetry, coherence, intensity, SNR, PCA, QGIS, SNAP, R studio, CATALYST, PCA, CATALYST, R studio, sentinel-1, UNOSAT, SNR, SNAP, coherence, war damage, Ukraine, intensity, QGIS, polarimetry, Settore ICAR/06 - Topografia e Cartografia
Published
2023

12. The Hyperspectral Prisma Mission in Operations

Author

Daraio Maria Girolamo, Guarini Rocchina, Lopinto Ettore, Caporusso Giacomo, Sacco Patrizia, Loizzo Rosa, and Lorusso Rino

Subjects
Project commissioning, Return on investment, Systems engineering, Hyperspectral imaging, Reflectivity, Phase (combat), Data policy
Abstract

PRISMA is an Italian EO hyperspectral Mission conceived as a Pre-operational and technology demonstrator. PRISMA is in orbit since March the 22nd 2019, has completed the Commissioning Phase in January 2020 and is currently in the operational phase. User registration to access PRISMA mission products has been opened on 21th of May 2020. Users can request new acquisitions on own AoI and/or archived products based on data acquired within the background mission. According to the PRISMA data policy, a wide use of products is allowed in order to validate the technology, maximize the return on investment and support the development of skills in an innovative sector. This paper reports an overview of the mission performances, scientific initiatives and first month user access results.

Published
2020

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