Neuro-Fuzzy Processing of Remote Sensed Data

Abstract

In this work the effectiveness of the Fuzzy Kohonen Clustering Network (FKCN) has been explored in two classification experiments of remote sensed data. The FKCN has been introduced in a multi-modular neural classification system for feature extraction before labelling. The unsupervised module is connected in cascade with the next supervised module based on the backpropagation learning rule. The performance of the FKCN has been evaluated in comparison with those of a conventional Kohonen Self Organizing Map (SOM) neural network. Experimental results have proved that the fuzzy clustering network can be used for complex data pre-processing, but further investigation on FKCN stop criterion is required.