CAJ | 학술논문

遥感影像是获取土地覆盖信息的重要手段。分析了影响决策树分类的特征因子，并根据这些因子构建分类决策树。结合中分辨率多源遥感数据，对贡嘎山区进行土地覆盖分类，通过与最大似然法分析对比，基于决策树的多源数据分类对试验区的分类精度（总体精度85.71%，Kappa系数0.83）远高于基于像素的最大似然法监督分类（总体精度63.56%，Kappa系数0.58）。
요감영상시획취토지복개신식적중요수단。분석료영향결책수분류적특정인자，병근거저사인자구건분류결책수。결합중분변솔다원요감수거，대공알산구진행토지복개분류，통과여최대사연법분석대비，기우결책수적다원수거분류대시험구적분류정도（총체정도85.71%，Kappa계수0.83）원고우기우상소적최대사연법감독분류（총체정도63.56%，Kappa계수0.58）。
Since it is an important approach to get land cover information,remote sensing provides services to resource surveys,environmental monitoring,etc. ,the study of remote sensing image classification is significant. This paper analyzes the factors affecting the characteristics of decision tree classification,and then the decision tree to classify the image was built based on these factors. Combined with medium reso-lution multi-source remote sensing data,taking Gongga Mountain for instance,the comparisons to the maximum likelihood method were performed for the validation. The result demonstrates that the classifica-tion accuracy of the test area( overall accuracy 85 . 71%,Kappa coefficient of 0 . 83 )is much higher than the pixel-based maximum likelihood classification( overall accuracy of 63 . 56%,kappa coefficient of 0 . 58 ),showing the advantages and prospects of the object-based multi-source data decision tree clas-sification .