A multi-temporal classification of multi-spectral images using a neural network

The classification of remotely sensed multi-spectral data using classical statistical methods has been worked on for several decades. There have been many new developments in neural network (NN) research, and many new applications have been studied. It is well known that NN approaches have the ability to classify without assuming a distribution. The authors previously proposed an NN model to combine the spectral and spatial information of LANDSAT TM images. In this paper, the authors apply the NN approach with a normalization method to classify multi-temporal LANDSAT TM images in order to investigate the robustness of their approach. From the authors' experiments, they confirm that their approach is more effective for the classification of multi-temporal data than the original NN approach and maximum likelihood approach.