Fuzzy contextual classification of multisource remote sensing images

Our objective has been to model satellite image classification as a cognitive process, providing a procedure that mimics the rich interaction of human activity in solving classification problems. The key features of this approach are the definition of a knowledge-based classification methodology designed to integrate contextual information into a multisource classification scheme, together with a fuzzy knowledge representation framework to model the overall process in a form that closely resembles the mental representation of human experts. An application for the identification of the glacier equilibrium line in two different zones of the Italian Alps has been developed to evaluate the performance of our methodology in a real domain where class discrimination requires the simultaneous use of contextual and multisource information. Numerical results are provided and compared with those obtained by a conventional classification procedure. The advantages of the approach, as seen in the experimental context, are examined.