中文摘要：

采用4种面向对象分类方法 （最邻近法、隶属度函数法、决策树和支持向量机）,利用SPOT5影像对湖南省会同县部分地区进行林地类型提取。结合研究区林地类型,将分类提取6种林地类型、6种非林地类型,并相应地构建分类层次结构。通过比较4种面向对象方法的分类结果,发现最邻近法擅长提取对象特征相近的地物类型,更适合于丘陵地区的林地信息提取。其在南方丘陵山区进行林地信息提取精度显著高于其他3种方法,其总体分类精度可达76.12%（分12类）,Kappa系数为0.73（分12类）。

英文摘要：

In this study,four kinds of object-oriented methods,including nearest neighbor method,a member function method,support vector machine and decision tree,are used for forest classification with SPOT5 image in Huitong county of Hunan Province.As the actual forest classes in Huitong county,6 forest classes and 6 non-forest classes were extracted in this study,and the classification hierarchy is also constructed.By comparing the forest classification results of the four object-oriented methods,it is found that the nearest neighbor method performed the best for forest classification,especially for those forest classes with similar object features,and it is more suitable for extracting forest classes in hilly area,its classification accuracy can reach 76.12%（12 classes）,its kappa coefficient can reach 0.73 （12 classes）in the mountainous and hilly areas of southern China,which are obviously higher than those of other methods.