Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario
This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Si...
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Istituto Nazionale di Geofisica e Vulcanologia, INGV
2015
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Online Access: | https://www.annalsofgeophysics.eu/index.php/annals/article/view/6638 https://doi.org/10.4401/ag-6638 |
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ftjaog:oai:ojs.annalsofgeophysics.eu:article/6638 2023-05-15T16:09:35+02:00 Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario Picchiani, Matteo Chini, Marco Corradini, Stefano Merucci, Luca Piscini, Alessandro Del Frate, Fabio 2015-03-03 application/pdf https://www.annalsofgeophysics.eu/index.php/annals/article/view/6638 https://doi.org/10.4401/ag-6638 eng eng Istituto Nazionale di Geofisica e Vulcanologia, INGV https://www.annalsofgeophysics.eu/index.php/annals/article/view/6638/6481 https://www.annalsofgeophysics.eu/index.php/annals/article/view/6638 doi:10.4401/ag-6638 Annals of Geophysics; V. 57 (2014): Fast Track 2: Atmospheric emissions from volcanoes Annals of Geophysics; Vol. 57 (2014): Fast Track 2: Atmospheric emissions from volcanoes 2037-416X 1593-5213 Neural Networks Volcanic Ash detection BTD Atmosphere info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2015 ftjaog https://doi.org/10.4401/ag-6638 2022-03-27T06:38:26Z This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Six classes have been defined paying particular attention to image regions that represent the different surfaces that could possibly be found under volcanic ash clouds. Complex cloudy scenarios composed by images collected during the Icelandic eruptions of the Eyjafjallajökull (2010) and Grimsvötn (2011) volcanoes have been considered as test cases. A sensitivity analysis on the MODIS TIR and VIS channels has been performed to optimize the algorithm. The neural network has been trained with the first image of the dataset, while the remaining data have been considered as independent validation sets. Finally, the neural network classifier’s results have been compared with maps classified with several interactive procedures performed in a consolidated operational framework. This comparison shows that the automatic methodology proposed achieves a very promising performance, showing an overall accuracy greater than 84%, for the Eyjafjalla - jökull event, and equal to 74% for the Grimsvötn event. Article in Journal/Newspaper Eyjafjallajökull Annals of Geophysics (INGV, Istituto Nazionale di Geofisica e Vulcanologia) Jökull ENVELOPE(-18.243,-18.243,65.333,65.333) Annals of Geophysics 57 |
institution |
Open Polar |
collection |
Annals of Geophysics (INGV, Istituto Nazionale di Geofisica e Vulcanologia) |
op_collection_id |
ftjaog |
language |
English |
topic |
Neural Networks Volcanic Ash detection BTD Atmosphere |
spellingShingle |
Neural Networks Volcanic Ash detection BTD Atmosphere Picchiani, Matteo Chini, Marco Corradini, Stefano Merucci, Luca Piscini, Alessandro Del Frate, Fabio Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario |
topic_facet |
Neural Networks Volcanic Ash detection BTD Atmosphere |
description |
This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Six classes have been defined paying particular attention to image regions that represent the different surfaces that could possibly be found under volcanic ash clouds. Complex cloudy scenarios composed by images collected during the Icelandic eruptions of the Eyjafjallajökull (2010) and Grimsvötn (2011) volcanoes have been considered as test cases. A sensitivity analysis on the MODIS TIR and VIS channels has been performed to optimize the algorithm. The neural network has been trained with the first image of the dataset, while the remaining data have been considered as independent validation sets. Finally, the neural network classifier’s results have been compared with maps classified with several interactive procedures performed in a consolidated operational framework. This comparison shows that the automatic methodology proposed achieves a very promising performance, showing an overall accuracy greater than 84%, for the Eyjafjalla - jökull event, and equal to 74% for the Grimsvötn event. |
format |
Article in Journal/Newspaper |
author |
Picchiani, Matteo Chini, Marco Corradini, Stefano Merucci, Luca Piscini, Alessandro Del Frate, Fabio |
author_facet |
Picchiani, Matteo Chini, Marco Corradini, Stefano Merucci, Luca Piscini, Alessandro Del Frate, Fabio |
author_sort |
Picchiani, Matteo |
title |
Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario |
title_short |
Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario |
title_full |
Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario |
title_fullStr |
Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario |
title_full_unstemmed |
Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario |
title_sort |
neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario |
publisher |
Istituto Nazionale di Geofisica e Vulcanologia, INGV |
publishDate |
2015 |
url |
https://www.annalsofgeophysics.eu/index.php/annals/article/view/6638 https://doi.org/10.4401/ag-6638 |
long_lat |
ENVELOPE(-18.243,-18.243,65.333,65.333) |
geographic |
Jökull |
geographic_facet |
Jökull |
genre |
Eyjafjallajökull |
genre_facet |
Eyjafjallajökull |
op_source |
Annals of Geophysics; V. 57 (2014): Fast Track 2: Atmospheric emissions from volcanoes Annals of Geophysics; Vol. 57 (2014): Fast Track 2: Atmospheric emissions from volcanoes 2037-416X 1593-5213 |
op_relation |
https://www.annalsofgeophysics.eu/index.php/annals/article/view/6638/6481 https://www.annalsofgeophysics.eu/index.php/annals/article/view/6638 doi:10.4401/ag-6638 |
op_doi |
https://doi.org/10.4401/ag-6638 |
container_title |
Annals of Geophysics |
container_volume |
57 |
_version_ |
1766405447872413696 |