Publication Date:
2017-04-04
Description:
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.
Description:
Published
Description:
1525-1535
Description:
5V. Sorveglianza vulcanica ed emergenze
Description:
JCR Journal
Description:
restricted
Keywords:
Image processing
;
Neural networks
;
fuzzy logic
;
Remote sensing of volcanoes
;
Hot-spot detection
;
Mt. Etna
;
04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
article
Permalink
