From Multi-Spectral to Hyper-Spectral Imagery: a Quantitative Analysis of the Improvements in Terms of Land Cover Classification

In this paper we report on the different land cover classification performances, over the same urban and rural landscape, of Multi-spectral Landsat ETM and Hyperspectral CHRIS-PROBA instruments. A Landsat ETM+ acquisition of 19 August 2004 and a CHRIS Proba image of 19 August 2006 over the same test area of Frascati-Tor Vergata have been considered. Even though a time shift of two years is present, the images have been acquired in the same day of the year, which makes them sufficiently suitable for the comparison. For the classification task a neural network algorithm is considered for both type of images. Indeed neural algorithms in last years have demonstrated at the same time a particular ease on managing a large domain of inputs and an important effectiveness in performing the decision task for the classification of remotely sensed images. A quantitative analysis in terms of classification accuracy on the different types of surfaces is carried out and the results critically analysed.