Decision fusion approach for multitemporal classification

This paper proposes two decision fusion-based multitemporal classifiers, namely, the jointly likelihood and the weighted majority fusion classifiers, that are derived using two different definitions of the minimum expected cost. Without any overhead incurred by multitemporal processing, a user-selected conventional pixelwise classifier makes local class decisions separately using each temporal data set, and the proposed multitemporal classifiers make the global class decisions by optimally summarizing those local class decisions. The proposed weighted majority decision fusion classifier can handle not only the data set reliabilities but also the classwise reliabilities of each data set. Classification experiment using the jointly likelihood decision fusion with three remotely sensed Thematic Mapper (TM) data sets shows more than 10% overall classification accuracy improvement over the pixelwise maximum likelihood classifier.