In this paper, we report the results of an investigation into the use of different neural models for the supervised classification of a multisensor (optical and radar) data set. We evaluated the performances of two well-known types of neural classifiers (i.e., MLPs, and Probabilistic Neural Networks (PNNs)) and compared them with the performances of the structured neural networks (SNNs) we proposed in [4, 5]. Further comparisons with the k-nearest neighbour classifier were also made in order to evaluate the validity of the considered neural networks as alternative classifiers to classical statistical ones.

Experimental comparison of neural networks for the classification of multisensor remote-sensing images

ROLI, FABIO;
1995

Abstract

In this paper, we report the results of an investigation into the use of different neural models for the supervised classification of a multisensor (optical and radar) data set. We evaluated the performances of two well-known types of neural classifiers (i.e., MLPs, and Probabilistic Neural Networks (PNNs)) and compared them with the performances of the structured neural networks (SNNs) we proposed in [4, 5]. Further comparisons with the k-nearest neighbour classifier were also made in order to evaluate the validity of the considered neural networks as alternative classifiers to classical statistical ones.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/180715
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