Neural net classifier for multi-temporal LANDSAT images using spacial and spectral information

The classification of remotely sensed multispectral data using classical statistical methods has been worked on for several decades. Recently 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. We have proposed an NN model to combine the spectral and spatial information. In this paper, we apply the NN approach to the classification of multi-temporal LANDSAT TM images in order to investigate the robustness of two normalization methods using spectral and spatial information. From our experiments, we confirmed that the NN approach with the preprocess is more effective for the classification than the original NN approach even if the test data is taken at the different time.