Classification of multisource and hyperspectral data based on decision fusion

Multisource classification methods based on neural networks and statistical modeling are considered. For these methods, the individual data sources are at first treated separately and modeled by statistical methods. Then several decision fusion schemes are applied to combine the information from the individual data sources. These schemes include weighted consensus theory where the weights of the individual data sources reflect the reliability of the sources. The weights are optimized in order to improve the combined classification accuracies. Other considered decision fusion schemes are based on two-stage approaches which use voting in the first stage and reject samples if either the majority or all of the classifiers for the data sources do not agree on a classification of a sample. For the second stage, a neural network is used to classify the rejected samples. The proposed methods are applied in the classification of multisource and hyperdimensional data sets, and the results compared to accuracies obtained with conventional classification schemes.