High Resolution Land Cover Image Classification and Evaluation Based on Fuzzy ARTMAP Neural Network
基于Fuzzy ARTMAP神经网络的高分辨率图象土地覆盖分类及其评价

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.