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Abstract
A comparison of change detection approaches for flooded area mapping using Synthetic Aperture Radar (SAR) images is provided. The aim was to assess the usefulness of fuzzy and neuro-fuzzy techniques for classification of SAR data. The work addresses both options of data-level fusion and decision-level fusion. The former is realized with multitemporal fuzzy or neural classification and the latter by combining classifications or fuzzy memberships for the pre- and post-event images. Highest overall accuracy values and flooded area accuracy values (90.3% producer's, 71.9% user's) were obtained from the neuro-fuzzy approach.
Acknowledgments
The authors want to thank Dr Francesco Zucca (Department of Earth Science) for his help. This work was supported by the Italian Space Agency, contracts I/R/193/02 and I/R/177/02.