Parallel consensual neural networks

A neural network architecture is proposed and applied in classification of remote sensing/geographic data from multiple sources. The architecture is called the parallel consensual neural network, and its relation to hierarchical and ensemble neural networks is discussed. The parallel consensual neural network architecture is based on statistical consensus theory. The input data are transformed several times. The different transformed data are applied as if they were independent inputs, and are classified using stage neural networks. The outputs from the stage networks are weighted and combined to make a decision. Experimental results based on remote sensing data and geographic data are given. The performance of the consensual neural network architecture is compared to that of a two-layer (one hidden layer) conjugate-gradient backpropagation neural network. The results compare favorably in terms of classification accuracy to the backpropagation method