自组织网络在遥感土地覆盖分类中应用研究

设计完成和比较了自组织网络的几种算法在遥感土地覆盖分类中的应用，结果表明非监督和监督学习结合方法进行遥感土地覆盖分类，各算法在分类性能上无显著差异，因此可采用算法和较简单的单竞争学习网络，根据最邻近原则进行非参数分类。

Landcover Classification of Remote Sensing Imagery Using Self organizing Neur al Network

In this peaper,the implement and comparison of different self-organizing learning algorithm in landcover clas- sification of Landsat TM imagery it is found that with the combination of unsupervised and supervised learning method and the nearest neighbour principle these algorithms have no significant difference in classification accuracy .The study result shows that the self-organizing network is an another method to classify the landcover type in remote sensing imagery by combining the unsupervised and supervised learning phase with the nearest neighbour principle .Because of the sim- plicitv of the Simple Competivite Learning the self-organizing network can use the Simple Competivite Learning algorithm in remotely sensed data classification.