In this peaper

the implement and comparison of different self organizing learn ing algorithm in landcover classification of Landsat TM imagery it is found that

with the combination of unsupervised and supervised learning method and the ne a rest neighbour principle

these algorithms have no significant difference in cla ssification accuracy. The study result shows that the self organizing network i s an another method to classify the landcover type in remote sensing imagery by combining the unsupervised and supervised learning phase with the nearest neighb our principle. Because of the simplicity of the Simple Competivite Learning

the self organizing network can use the Simple Competivite Learning algorithm in re motely sensed data classification.