Combination of multiple classifiers by fuzzy integrals: an application to synthetic aperture radar (SAR) data

In this work, the results obtained in the classification of a multi-source - multi-temporal remote sensed data set by means of a distributed neuro-fuzzy system are compared with the results of a traditional centralized neural classification system, based on a single multilayer perceptron (MLP) neural network module. The distributed system is composed by a set of neural classifiers, whose partial results were combined with both Sugeno and Choquet fuzzy integrals. Two classification experiments were carried out with the distributed system. In the first experiment, each neural module of the distributed system used the same learning rule but was trained with a subset of the input features, i.e., a specific spectral band. In the second experiment, the neural modules of the system were trained with the same complete set of input features available for each training pixel, but consisted of MLP networks characterized by different specific topologies or different neural algorithms. The results show that larger improvements can be obtained by combining more independent classifiers. The Choquet fuzzy integral provided better performance than Sugeno fuzzy integral. The centralized system, based on a single MLP module, provided the best classification performance.