Classification of multisensor remote-sensing images by multiple structured neural networks

Recently, a new class of structured neural networks (SNNs), explicitly devoted to multisensor remote-sensing image classification and aimed at allowing the interpretation of the “network behaviour”, was proposed. Experiments reported pointed out that SNNs provide a trade off between classification accuracy and interpretation of the network behaviour. In this paper, the combination of multiple SNNs, each of which has been trained on the same data set, is proposed as a means to improve the classification results, while keeping the possibility of interpreting the network behaviour