Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/9710
AuthorsPicchiani, M.* 
Chini, M.* 
Corradini, S.* 
Merucci, L.* 
Piscini, A.* 
Del Frate, F.* 
TitleNeural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario
Issue Date2014
Series/Report no.fast track 2/57(2014)
DOI10.4401/ag-6638
URIhttp://hdl.handle.net/2122/9710
Keywordsremote sensing; ash detection; neural networks; MODIS
Subject Classification04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring 
04. Solid Earth::04.08. Volcanology::04.08.07. Instruments and techniques 
05. General::05.02. Data dissemination::05.02.03. Volcanic eruptions 
AbstractThis 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 Eyjafjallajökull event, and equal to 74% for the Grimsvötn event.
Appears in Collections:Papers Published / Papers in press

Files in This Item:
File Description SizeFormat 
Picchiani_6638_ok.pdf620.01 kBAdobe PDFView/Open
Show full item record

Page view(s)

45
checked on May 26, 2017

Download(s)

52
checked on May 26, 2017

Google ScholarTM

Check

Altmetric