Combining SOFM And Expert Classifier For Land Types Classification Of Remotely Sensed Data

On the basis of introducing the main methods and achievements in the field of land types classification based on remote sensing image in China and in abroad, the paper mainly introduces the features of artificial neural network and application in classification of remote sening.The paper combined the self-organizing feature maps with expert classification to classify the land use type. Because of same object with different spectra and different objects with same spectrum, firstly the study analysis the modulus of object. The self-organizing feature maps(SOFM) is an Unsupervised learning classification method, so the number of nodes of output layer should equaled approximately the modulus of object when we applicate SOFM. Then the study applicate expert classifier to classify again.Secondly, Taking into account the effect of sample randomnes and the feature of ground object, equal sample points were drawn in different land use types of the result to evaluate the classification precision. The result of evaluation shows that SOFM could improve the accuracy the classification greatly than the traditional unsupervised classification.Lastly, the study give some methods and suggestions to improve the accuracy of classification. Due to different classification method have the feature of uniformity, the study conbime self-organizing feature maps with expert classification to realize the joint of Unsupervised learning and previous experience.