PARALLEL CONSENSUAL NEURAL NETWORKS

A new type of a neural-network architecture, the parallel consensual n eural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN ar chitecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual derision. In this paper, optimization methods are used in order to we ight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and a nother for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural net work and conventional statistical methods in terms of overall classifi cation accuracy of test data.