Principal Component Analysis (PCA) is a method based on statistics and linear algebra
techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in …

In this manuscript, we address the problem of change detection for Sentinel-2 data. The
proposed method is based on deep features representation. First, multilevel convolutional …

We have investigated the possible cause-and-effect relationship due to stress transfer between
two earthquakes that occurred near Christchurch, New Zealand, in September 2010 and …

Accurate automatic volcanic cloud detection by means of satellite data is a challenging task
and is of great concern for both the scientific community and aviation stakeholders due to …

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 basic idea of this paper relies on the concurrent exploitation of the capabilities of neural
networks (NNs) and SAR interferometry (InSAR) for the characterization of a seismic source …

In this work the volcanic ash and SO2 retrievals obtained by applying three different
procedures (LUT-Look Up Table, NN-Neural Network and VPR-Volcanic Plume Removal) on …

In this article, a new technique for features extraction from SAR interferograms is presented.
The technique combines the properties of auto-associative neural networks with those of …

During a volcanic eruption, large quantities of Sulphur dioxide (SO2) are sometimes emitted
into the atmosphere. Rapid detection and tracking ofvolcanic SO2 clouds might be …

Satellite SAR Interferometry (InSAR) has already proved its effectiveness in the analysis of
seismic events. In fact, measuring the surface displacement field generated by an earthquake …