Correlated probabilities based decision fusion method for multi-sensor data

Most of the decision fusion techniques developed for the remote sensing applications have the drawback of assuming the conditional independence between the classification results, whereas, usually the correlation exists due to the same measuring instrument or same area under study. Fusion of Correlated Probabilities (FCP) method has a potential to deal with conditional dependence only for two data sets. The proposed Multi-sensor Fusion of Correlated Probabilities (MFCP) algorithm is the extended version of FCP by modifying the cross conditional dependence between three or more multi-sensor information sources. The proposed MFCP method is assessed in the multi-sensor land cover classification over the Beijing area for three sensor data sets. We evaluated and validated our proposed methodology by comparing it with four existing fusion methods. The experimental results demonstrated that the proposed MFCP method outperformed all the compared fusion methods in terms of overall accuracy, kappa and class wise accuracies. Therefore, the MFCP method is adaptable to any type and any number of sensor data sets.