This article presents a new approach to pattern recognition of multicategory remote sensing images by using multilayered neural network and self-organizing neural network. BP algorithm is a typical supervised classification method by which multilayered neural network can learn previous knowledge about patterns of remote sense image from trainning sample set and form complicated nonlinear decision function automatically. Kohonen neural network

which may produce what is called self-organizing feature maps similar to those that occur in the human brain

can be used as an unsupervised classifier. In this article

considering the features of multicategory remote sensing images

we synthesize the advantages of these two methods to form a complex classifier. First

trainning samples are divided into several groups to train the corresponding BP networks which parallel to construct supervised classifier. Second

the remote sensing image is classified into many gross classes by the supervised classifier. Third

use the result of the supervised classifier to train Kohonen network and each gross class is classified into some sub-classes by the trained Kohonen network. This method has been used in the classification of a SPOT remote sensing image

the number of recognizable classes is 48 while the average right rate of the supervised classification is 91.6%. The experimental results verify the usefulness of this approach.