CAJ | 학술논문

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）。
채용4충면향대상분류방법（최린근법、대속도함수법、결책수화지지향량궤），이용SPOT5영상대호남성회동현부분지구진행임지류형제취。결합연구구임지류형，장분류제취6충임지류형、6충비임지류형，병상응지구건분류층차결구。통과비교4충면향대상방법적분류결과，발현최린근법천장제취대상특정상근적지물류형，경괄합우구릉지구적임지신식제취。기재남방구릉산구진행임지신식제취정도현저고우기타3충방법，기총체분류정도가체76.12％（분12류），Kappa계수위0.73（분12류）。
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 for-est 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 clas-ses)in the mountainous and hilly areas of southern China,which are obviously higher than those of other methods.