Neural refinement strategy for a fuzzy Dempster-Shafer classifier of multisource remote sensing images

Elisabetta Binaghi; Paolo Madella; Ignazio Gallo; Anna Rampini

doi:10.1117/12.331866

4 December 1998 Neural refinement strategy for a fuzzy Dempster-Shafer classifier of multisource remote sensing images

Elisabetta Binaghi, Paolo Madella, Ignazio Gallo, Anna Rampini

Proceedings Volume 3500, Image and Signal Processing for Remote Sensing IV; (1998) https://doi.org/10.1117/12.331866
Event: Remote Sensing, 1998, Barcelona, Spain

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.

Citation Download Citation

Elisabetta Binaghi, Paolo Madella, Ignazio Gallo, and Anna Rampini "Neural refinement strategy for a fuzzy Dempster-Shafer classifier of multisource remote sensing images", Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); https://doi.org/10.1117/12.331866

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