Fusion of optical and multifrequency polsar data for forest classification

In this study a decision fusion strategy is proposed for a multisensor data fusion process using optical and polarimetric multifrequency synthetic aperture radar data for forest classification. Instead of utilizing one classifier for all available features, grouped features are classified by using individual classifiers. A qualified majority voting (QMV) consensual rule, derived from the confusion matrix is utilized in the subsequent fusion process to combine decisions. With this approach, more consistent results are obtained than using all features as a stacked vector and maximum likelihood classification (MLC).