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Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/9710

Authors: Picchiani, M.*
Chini, M.*
Corradini, S.*
Merucci, L.*
Piscini, A.*
Del Frate, F.*
Title: Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario
Title of journal: Annals of Geophysics
Series/Report no.: fast track 2/57(2014)
Issue Date: 2014
DOI: 10.4401/ag-6638
Keywords: remote sensing; ash detection; neural networks; MODIS
Abstract: 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 Eyjafjallajökull event, and equal to 74% for the Grimsvötn event.
Appears in Collections:04.08.07. Instruments and techniques
05.02.03. Volcanic eruptions
04.08.06. Volcano monitoring
Papers Published / Papers in press

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