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A New Approach for Multi-System/Sensor Decision Fusion Based on Joint Measures

This article describes the joint measures method as a new powerful method for the development of a high performance multi-sensor data/image fusion scheme at the decision level. The images are received from distributed multiple sensors, which sense the targets in different spectral bands including visible, infrared, thermal and microwave. At first, we study the decision fusion methods, including voting schemes, rank based algorithm, Bayesian inference, and the Dempster-Shafer method. Then, we extract the mathematical properties of multi-sensor local classification results and use them for modeling of the classifier performances by the two new measures, i.e. the plausibility and correctness. Then we establish the plausibility and correctness distribution vectors and matrices for introducing the two improvements of the Dempster-Shafer method, i.e. the DS (CM) and DS (PM) methods. After that we introduce the joint measures decision fusion method based on using these two measures jointly. The Joint Measures Method (JMM) can deal with any decision fusion problem in the case of uncertain local classifiers results as well as clear local classifiers results. Finally, we deploy the new and previous methods for the fusion of the two different sets of multispectral image classification local results and we also compare their reliabilities, the commission errors and the omission errors. The results obviously show that the DS (PM), DS (CM) and JMM methods which use the special properties of the local classifiers and classes, have much better accuracies and reliabilities than other methods. In addition, we show that the reliability of the JMM is at least 3% higher than all other methods.