Fusion of low resolution optical and high resolution SAR data for land cover classification

A set of ERS SAR and optical MODIS-images were classified to land cover and tree species classes. Different methods for pixel and decision based data fusion were tested. Classifications of featuresets were carried out using Bayes rule for minimum error. The results were not very successful, the classification accuracies of land cover classes varied from 43% to 7.5%, depending on the used features and classes. The decision based data fusion method, where the a'posteriori probabilities representing the proportions of different land cover classes of low resolution classification are used as a'prior probabilities in high resolution classification looks promising. Using this method, the increase of overall and classwise accuracies can be more than 10 and 25 %-units, respectively.