欢迎访问林草资源研究

林业资源管理 ›› 2014›› Issue (5): 92-99.doi: 10.13466/j.cnki.lyzygl.2014.05.017

• 科学研究 • 上一篇    下一篇

基于面向对象方法和SPOT5的丘陵山区林地分类研究

杨飞1, 刘丽峰2, 王学成2   

  1. 1.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101;
    2.山东理工大学 建筑工程学院,山东 淄博 255000
  • 收稿日期:2014-08-06 修回日期:2014-09-29 出版日期:2014-10-28 发布日期:2020-11-23
  • 作者简介:杨飞(1981-),男,山东枣庄人,助理研究员,博士,主要从事遥感与GIS应用,以及生态环境安全方面的研究工作。Email:yangfei@igsnrr.ac.cn
  • 基金资助:
    国家自然科学青年基金项目(41301607);科技部国家科技基础性工作专项(2012FY111800)

Study on Forest Classification Based on Object-oriented Method and SPOT5 Images in Hilly Mountain Area

YANG Fei1, LIU Lifeng2, WANG Xuecheng2   

  1. 1. Institute of Geographic Sciences and Natural Resources Research,CAS,State Key Laboratory of Resources and Environmental Information System,Beijing 100101,China;
    2. Shandong University of Technology,School of Architectural Engineering,Zibo,Shandong 255000,China
  • Received:2014-08-06 Revised:2014-09-29 Online:2014-10-28 Published:2020-11-23

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)。

关键词: 面向对象方法, 丘陵山区,林地分类

Abstract: 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.

Key words: object-oriented method, hilly mountain area, forest classification

中图分类号: