The work focuses on evaluating quality and estimating information of CHRIS hyper-spectral images. Quality is assessed through the characterisation of the noise while information is estimated by means of an operative definition according to which the information content of a data set is given by the amount of information that cannot be predicted from the data that have already been acquired and,...

We investigate the statistical modeling of hyper-spectral data. The accurate modeling of experimental data is critical in target detection and classification applications. In fact, having a statistical model that is capable of properly describing data variability leads to the derivation of the best decision strategies together with a reliable assessment of algorithm performance. Most existing c...

Polar satellite measurements provide frequent overpass on the Arctic area and high spatial resolution, but the cloud parameter retrieval and their detection is very difficult at high latitudes. In great part the Arctic surface is covered by snow and ice, reducing the visible contrast between clouds and the surface. Also, often there are strong surface temperature inversions and during the winte...

The wide availability of multispectral images has fostered the development of new algorithms for remote sensing applications. These applications range from agricultural and environmental to military use. Nevertheless, the analysis of such voluminous data requires advanced analysis and computational methodologies as well as advanced hardware and computational methods. In this paper we introduce ...