Share Email Print
cover

Proceedings Paper

Neural refinement strategy for a fuzzy Dempster-Shafer classifier of multisource remote sensing images
Author(s): Elisabetta Binaghi; Paolo Madella; Ignazio Gallo; Anna Rampini
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

This paper presents a hybrid strategy for the classification of multisource remote sensing images basing on a knowledge representation framework which integrates fuzzy logic and Dempster-Shafer theory and is capable of dealing with possibilistic and credibilistic forms of uncertainty in an unified way. Within the strategy, the salient, innovative aspect here proposed is the use of a novel neural network model for refinement of fuzzy Dempster-Shafer classification rules. The approach has been evaluated by developing real- world applications in the field of water vulnerability assessment and fire risk assessment. Numerical results obtained show that classification benefit from the integration of neural and symbolic frameworks.

Paper Details

Date Published: 4 December 1998
PDF: 11 pages
Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); doi: 10.1117/12.331866
Show Author Affiliations
Elisabetta Binaghi, Istituto per le Tecnologie Informatiche Multimediali/CNR (Italy)
Paolo Madella, Istituto per le Tecnologie Informatiche Multimediali/CNR (Italy)
Ignazio Gallo, Istituto per le Tecnologie Informatiche Multimediali/CNR (Italy)
Anna Rampini, Istituto per le Tecnologie Informatiche Multimediali/CNR (Italy)


Published in SPIE Proceedings Vol. 3500:
Image and Signal Processing for Remote Sensing IV
Sebastiano Bruno Serpico, Editor(s)

© SPIE. Terms of Use
Back to Top