CAJ | 학술논문

本研究利用面向对象分类方法和Rapideye高分辨率遥感影像对广东省肇庆市部分地区进行自动遥感分类进而提取桉树空间分布信息，并将提取结果与传统的监督分类方法(最小距离法)提取结果进行对比分析。本研究主要对桉树等5种林地类型以及草地、其它用地等共7种地物类型进行分类提取。通过对比面向对象方法和最小距离法分类结果，发现基于面向对象分类方法提取桉树林地结果较好，分类精度可达82.08%，Kappa系数可达0.74，分类效果显著优于传统的监督分类方法，面向对象方法比较适合于桉树信息的分类提取。
본연구이용면향대상분류방법화Rapideye고분변솔요감영상대광동성조경시부분지구진행자동요감분류진이제취안수공간분포신식，병장제취결과여전통적감독분류방법(최소거리법)제취결과진행대비분석。본연구주요대안수등5충임지류형이급초지、기타용지등공7충지물류형진행분류제취。통과대비면향대상방법화최소거리법분류결과，발현기우면향대상분류방법제취안수임지결과교호，분류정도가체82.08%，Kappa계수가체0.74，분류효과현저우우전통적감독분류방법，면향대상방법비교괄합우안수신식적분류제취。
In this study, the object-oriented classification method and Rapideye high resolution remote sensing images were used to examine the spatial distribution ofEucalyptus and compare to the traditional supervised classification method (minimum distance). This study involved classification of 7 land cover types, which included 5 classes of forest (includingEucalyptus plantations), grass and other land uses. By comparing the results of objected-oriented method with minimum distance method, we found that objection-oriented classification showed obviously better performance on extractingEucalyptus plantation cover measures and classification of forest type than the minimum distance method, its accuracy of general classification can reach 82.1% and its Kappa coefficient can reach 0.74.