Multi-Spectral Satellite Image Classification Using an Evolving Neural Network Approach

Abstract

This paper investigates a new evolving neural network classifier based on real coded genetic algorithm for automatic multi-spectral satellite image classification (land cover mapping problem). The evolving neural network classifier is designed using hybrid genetic operators with classification accuracy as a measure of performance. The evolving neural network methodology is implemented in Pentium clusters. The proposed methodology searches for the best neural network architecture and its connection weights for a given set of training patterns. The performance of the proposed evolving neural network based classifier is evaluated for Level-II classifier model using the Landsat 7 Thematic Mapper high resolution imagery. After evolving the neural network at pixel level, the system performance is tested with sites not seen during training. Results are compared with maximum likelihood classifier, gradient based fully connected multilayer perceptron and growing and pruning radial basis function classifier. The proposed classifier is more accurate, robust with respect to the noise in the input spectrum and also overcomes the common limitations of the standard neural based classifier models.