Fabio Dell'Acqua

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Very high resolution hyperspectral data should be very useful to provide detailed maps of urban land cover. In order to provide such maps, both accurate and precise classification tools need, however, to be developed. In this letter, new methods for classification of hyperspectral remote sensing data are investigated, with the primary focus on multiple(More)
We investigate the use of co-occurrence texture measures to provide information on different building densities inside a town structure. We try to improve the pixel-by-pixel classification of an urban area by considering texture measures as a means for block analysis and classification. We find some interesting hints concerning the optimal dimension of the(More)
Analysis and reconstruction of range images usually focuses on complex objects completely contained in the field of view; little attention has been devoted so far to the reconstruction of simply-shaped wide areas like parts of a wall hidden behind furniture pieces in an indoor range image. The work presented in this paper is aimed at such reconstruction.(More)
In this paper, we present how we used neural networks (NNs) and a pyramidal approach to model the data obtained by a weather radar and to short-range forecast the rainfall behavior. Very short-range forecast is useful, for instance, for estimating the path attenuation in terrestrial point-to-point communications. Radial basis function NNs are used both to(More)
In this work, we present a fuzzy approach to the analysis of airborne synthetic aperture radar (SAR) images of urban environments. In particular, we want to show how to implement structure extraction algorithms based on fuzzy clustering unsupervised approaches. To this aim, the idea is to segment first the sensed data and recognize very basic urban classes(More)