Volcanic hot spot detection from optical multispectral remote sensing data using artificial neural networks

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.