By Topic

Fuzzy contextual classification of multisource remote sensing images

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
E. Binaghi ; Istituto per le Tecnologie Inf. Multimediali, CNR, Milano, Italy ; P. Madella ; M. Grazia Montesano ; A. Rampini

The authors' 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 their 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

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:35 ,  Issue: 2 )