Land use analysis of remote sensing data by Kohonen nets

The neural network approach by the backpropagation method (BPM) to land-use has been discussed in recent years. Using such a method, the accuracy depends on a training data set which has been selected manually. It takes much time to select a suitable training data set. In this paper, as a preprocessing of classifications the authors use the Kohonen feature map (KFM) and the competitive learning (CL) to get the better training data set. As a first step, the KFM that takes the Landsat TM data as input is adopted to form a rough classification of the wide area based on the observed data. In the next step, the CL whose inputs are the weights of the KFM node data is carried out to determine the category of each node of the KFM. The first weight set of the CL is a set on weights at four corners of the KFM. The combination of the two neural network techniques enables the authors to determine the rough land-use of an object region automatically. After that, the classification results by the KFM and the CL are further classified into more fine items by using the BPM. Finally, the classification results have been compared with other methods.