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Authors: Piscini, A.* 
Lombardo, V.* 
Title: Volcanic hot spot detection from optical multispectral remote sensing data using artificial neural networks
Issue Date: 16-Jan-2014
Series/Report no.: /196(2014)
DOI: 10.1093/gji/ggt506
Keywords: Image processing
Neural networks
fuzzy logic
Remote sensing of volcanoes
Hot-spot detection
Mt. Etna
Subject Classification04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring 
Abstract: This paper describes an application of artificial neural networks for the recognition of volcanic lava flow hot spots using remote sensing data. Satellite remote sensing is a very effective and safe way to monitor volcanic eruptions in order to safeguard the environment and the people affected by such natural hazards. Neural networks are an effective and well-established technique for the classification of satellite images. In addition, once well trained, they prove to be very fast in the application stage. In our study a back propagation neural network was used for the recognition of thermal anomalies affecting hot lava pixels. The network was trained using the three thermal channels of the Advanced Very High Resolution Radiometer (AVHRR) sensor as inputs and the corre- sponding values of heat flux, estimated using a two thermal component model, as reference outputs. As a case study the volcano Etna (Eastern Sicily, Italy) was chosen, and in particular the effusive eruption which took place during the month of 2006 July. The neural network was trained with a time-series of 15 images (12 nighttime images and 3 daytime images) and validated on three independent data sets of AVHRR images of the same eruption and on two relative to an eruption occurred the following month. While for both nighttime and daytime validation images the neural network identified the image pixels affected by hot lava with a 100 per cent success rate, for the daytime images also adjacent pixels were included, apparently not interested by lava flow. Despite these performance differences under different illumination conditions, the proposed method can be considered effective both in terms of classification accuracy and generalization capability. In particular our approach proved to be robust in the rejection of false positives, often corresponding to noisy or cloudy pixels, whose presence in multispectral images can often undermine the performance of traditional classification algorithms. Future work shall address application of the proposed method to data acquired with a high temporal resolution, such as those provided by the spinning enhanced visible and infrared imager sensor on board the Meteosat second generation geostationary satellite.
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