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)
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ftdoajarticles:oai:doaj.org/article:9efbea6f566d43bb9085e707483499b3 2023-05-15T16:09:36+02:00 Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario Matteo Picchiani Marco Chini Stefano Corradini Luca Merucci Alessandro Piscini Fabio Del Frate 2015-03-01T00:00:00Z https://doi.org/10.4401/ag-6638 https://doaj.org/article/9efbea6f566d43bb9085e707483499b3 EN eng Istituto Nazionale di Geofisica e Vulcanologia (INGV) http://www.annalsofgeophysics.eu/index.php/annals/article/view/6638 https://doaj.org/toc/1593-5213 https://doaj.org/toc/2037-416X 1593-5213 2037-416X doi:10.4401/ag-6638 https://doaj.org/article/9efbea6f566d43bb9085e707483499b3 Annals of Geophysics, Vol 57, Iss 0 (2015) Neural Networks Volcanic Ash detection BTD Meteorology. Climatology QC851-999 Geophysics. Cosmic physics QC801-809 article 2015 ftdoajarticles https://doi.org/10.4401/ag-6638 2022-12-30T21:53:55Z 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 Directory of Open Access Journals: DOAJ Articles Jökull ENVELOPE(-18.243,-18.243,65.333,65.333) Annals of Geophysics 57 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Neural Networks Volcanic Ash detection BTD Meteorology. Climatology QC851-999 Geophysics. Cosmic physics QC801-809 |
spellingShingle |
Neural Networks Volcanic Ash detection BTD Meteorology. Climatology QC851-999 Geophysics. Cosmic physics QC801-809 Matteo Picchiani Marco Chini Stefano Corradini Luca Merucci Alessandro Piscini Fabio Del Frate Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario |
topic_facet |
Neural Networks Volcanic Ash detection BTD Meteorology. Climatology QC851-999 Geophysics. Cosmic physics QC801-809 |
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 |
Matteo Picchiani Marco Chini Stefano Corradini Luca Merucci Alessandro Piscini Fabio Del Frate |
author_facet |
Matteo Picchiani Marco Chini Stefano Corradini Luca Merucci Alessandro Piscini Fabio Del Frate |
author_sort |
Matteo Picchiani |
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://doi.org/10.4401/ag-6638 https://doaj.org/article/9efbea6f566d43bb9085e707483499b3 |
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, Vol 57, Iss 0 (2015) |
op_relation |
http://www.annalsofgeophysics.eu/index.php/annals/article/view/6638 https://doaj.org/toc/1593-5213 https://doaj.org/toc/2037-416X 1593-5213 2037-416X doi:10.4401/ag-6638 https://doaj.org/article/9efbea6f566d43bb9085e707483499b3 |
op_doi |
https://doi.org/10.4401/ag-6638 |
container_title |
Annals of Geophysics |
container_volume |
57 |
_version_ |
1766405452620365824 |