主要讨论了基于Fuzzy ARTMAP神经网络的高分辨率遥感图象土地覆盖分类方法及其实践．首先介绍了Fuzzy ARTMAP神经网络的原理，然后用SPOT XS图象试验数据进行土地覆盖分类．分类结果与传统的最大似然监督分类(MLC)、反馈式(Back Propagation，BP)神经网络的分类结果进行了比较．通过抽取500个样点对3种分类结果进行精度评价表明，Fuzzy ARTMAP神经网络相对其他两种方法，分类精度均有不同程度的改善，具有更好的分类结果，总分类精度比MLC和BP算法分别提高17．41％、7．32％．最后，对不同分类方法对于土地覆盖分类结果的影响进行了评价和分析．试验表明，Fuzzy ARTMAP神经网络用于高分辨图象土地覆盖分类研究可以获得相对较好的分类结果．

This paper mainly discussed a high resolution land cover Image classification algorithm based on Fuzzy ARTMAP Neural Network, experiment and it's evaluation. We firstly introduced the fundamental theory of Fuzzy ARTMAP Neural Network classifier. Followed is a land cover classification experiment on SPOT XS high resolution image. Three algorithms were tested: the Maximum likelihood Classification (MLC), the Back Propagation (BP) Neural Network, and the Fuzzy ARTMAP Neural Network. Individual classification result was presented. We compared these different classification results and evaluated their accuracy through manually interpreting five hundred of randomly selected sample points. Our assessment shows that Fuzzy ARTMAP has a comparably better result, with overall classification accuracy higher 17 41%, 7 32% than MLC and BP. We also analyzed some misclassification between tillage and forest classes by different classification methodologies and gave some explanations. Finally, a superiority of the Fuzzy ARTMAP Neural Network classifier on high resolution land cover classification is concluded.