This paper proposes the application of structured neural networks to classification of multisensor remote-sensing images, The purpose of our approach is to allow the interpretation of the ''network behavior,'' as it can be utilized by photointerpreters for the validation of the neural classifier. In addition, our approach gives a criterion for defining the network architecture, so avoiding the classical trial-and-error process, First of all, the architecture of structured multilayer feedforward networks is tailored to a multisensor classification problem. Then, such networks are trained to solve the problem by the error backpropagation algorithm, Finally, they are transformed into equivalent networks to obtain a simplified representation. The resulting equivalent networks may be interpreted as a hierarchical arrangement of ''committees'' that accomplish the classification task by checking on a set of explicit constraints on input data, Experimental results on a multisensor (optical and SAR) data set are described in terms of both classification accuracy and network interpretation. Comparisons with fully connected neural networks and with the kappa-nearest neighbor classifier are also made.
CLASSIFICATION OF MULTISENSOR REMOTE-SENSING IMAGES BY STRUCTURED NEURAL NETWORKS
ROLI, FABIO
1995-01-01
Abstract
This paper proposes the application of structured neural networks to classification of multisensor remote-sensing images, The purpose of our approach is to allow the interpretation of the ''network behavior,'' as it can be utilized by photointerpreters for the validation of the neural classifier. In addition, our approach gives a criterion for defining the network architecture, so avoiding the classical trial-and-error process, First of all, the architecture of structured multilayer feedforward networks is tailored to a multisensor classification problem. Then, such networks are trained to solve the problem by the error backpropagation algorithm, Finally, they are transformed into equivalent networks to obtain a simplified representation. The resulting equivalent networks may be interpreted as a hierarchical arrangement of ''committees'' that accomplish the classification task by checking on a set of explicit constraints on input data, Experimental results on a multisensor (optical and SAR) data set are described in terms of both classification accuracy and network interpretation. Comparisons with fully connected neural networks and with the kappa-nearest neighbor classifier are also made.
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