Decision fusion strategies for SAR image target recognition

In this study, three decision fusion strategies in synthetic aperture radar (SAR) image target recognition for classification of ground vehicles in the Moving and Stationary Target Acquisition and Recognition public release database are proposed. These strategies include multi-view decision fusion strategy, multi-feature decision fusion strategy and multi-classifier decision fusion strategy. In multi-view decision fusion strategy, each view implements its recognition process individually and provides only its decision from the classifier to the fuser. In multi-feature decision fusion strategy, a variety of feature extraction algorithms are used to extract features from an image separately and each of the extracted feature vectors is submitted to a classifier. The decisions from the classifiers are then provided to the fuser. In multi-classifier decision fusion strategy, different classifiers are used to classify a feature vector. Each classifier executes its classification process individually and then provides its decision to the fuser. The fuser combines decisions to produce a single fused final decision in these three strategies. The three proposed strategies are evaluated and verified with experiments. Experimental results indicate that there are significant target recognition performance benefits in the probability of correct classification, when the proposed decision fusion strategies are properly applied in SAR image target recognition.