抄録
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 LAND-SAT-5 Thematic Mapper (TM) image data which has been available since 1984. Using the 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 take the spatial and spectral information into consideration, and we investigate the utility of this approach for classification. We confirmed that the NN approach behaves similar to human perceived classification.
