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Published in: Soft Computing 12/2011

01-12-2011 | Focus

A multistage genetic fuzzy classifier for land cover classification from satellite imagery

Authors: D. G. Stavrakoudis, J. B. Theocharis, G. C. Zalidis

Published in: Soft Computing | Issue 12/2011

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Abstract

A linguistic boosted genetic fuzzy classifier (LiBGFC) is proposed in this paper for land cover classification from multispectral images. The LiBGFC is a three-stage process, aiming at effectively tackling the interpretability versus accuracy tradeoff problem. The first stage iteratively generates fuzzy rules, as directed by a boosting algorithm that localizes new rules in uncovered subspaces of the feature space, implicitly preserving the cooperation with previously derived ones. Each rule is able to select the required features, further improving the interpretability of the obtained model. Special provision is taken in the formulation of the fitness function to avoid the creation of redundant rules. A simplification stage follows the first one aiming at further improving the interpretability of the initial rule base, providing a more compact and interpretable solution. Finally, a genetic tuning stage fine tunes the fuzzy sets database improving the classification performance of the obtained model. The LiBGFC is tested using an IKONOS multispectral VHR image, in a lake-wetland ecosystem of international importance. The results indicate the effectiveness of the proposed system in handling multidimensional feature spaces, producing easily understandable fuzzy models.

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Metadata
Title
A multistage genetic fuzzy classifier for land cover classification from satellite imagery
Authors
D. G. Stavrakoudis
J. B. Theocharis
G. C. Zalidis
Publication date
01-12-2011
Publisher
Springer-Verlag
Published in
Soft Computing / Issue 12/2011
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-010-0666-z

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