A neural network classifier for LANDSAT image data

There have been many new developments in neural network (NN) research, and many new applications have been studied. The classification of remotely sensed multispectral data using classical statistical methods has been worked on for several decades. Among the multispectral data, we concentrate on the Landsat-5 Thematic Mapper (TM) image data which has been available since 1984. Using this classical maximum likelihood approach, a category is modeled as a multivariate normal distribution; however, the distribution for Landsat images is unknown. It is well known that NN approaches have the ability to classify without assuming a distribution. We apply the NN approach to the classification of Landsat TM images in order to investigate the robustness of this approach for multi-temporal data classification. The authors confirmed that the NN approach is effective for the classification even if the test data is taken at the different time