According to the biological basis

basic structure and learning algorithms of Kohonen network

an image classification method is introduced.Pre-processing the image with principal component analysis method based on spectral characteristics of the land use types in the experimental area

and training Kohonen self-organization mapping with geographical ancillary data

land use classifications of Kohonen network are made by integrating image with geographic ancillary data.The classification results are analyzed and compared with the results obtained by Back-Propagation neural network and Maximum Likelihood.The result shows that the classification with geographic ancillary data can improve the image classification accuracy of Kohonen network.