Completely automatic classification of satellite multi-spectral imagery for the production of land cover maps

The increasing number of satellite missions providing more and more data for updating land cover and land use maps requires to upgrade the level of automatism for the processing of remotely sensed imagery. In this paper we try to pursue the ambitious goal of designing a completely automatic (no human interaction) supervised scheme for the classification, in terms of land cover, of a multi-spectral image. An expert system, using appropriate spectral and textural features, drives the selection of suitable training pixels in the image. These are used for the learning of a neural network algorithm that successively performs the pixel-based land cover classification of the whole image. The processing scheme has been tested on a set of Landsat images taken on different European urban areas.