By Topic

Classification of multisensor remote-sensing images by multiple structured neural networks

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
$31 $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

3 Author(s)
Roli, F. ; Dept. of Electr. & Electron. Eng., Cagliari Univ., Italy ; Serpico, S.B. ; Bruzzone, L.

Recently, a new class of structured neural networks (SNNs), explicitly devoted to multisensor remote-sensing image classification and aimed at allowing the interpretation of the “network behaviour”, was proposed. Experiments reported pointed out that SNNs provide a trade off between classification accuracy and interpretation of the network behaviour. In this paper, the combination of multiple SNNs, each of which has been trained on the same data set, is proposed as a means to improve the classification results, while keeping the possibility of interpreting the network behaviour

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996