摘要
利用TM、SPOT及CBERS-1等中分辨率卫星图像,对土地覆被的专家系统分类方法、居民地的决策树提取方法以及水体的迭代混合提取方法进行了试验,其总体精度达到87.89%,与常用的监督分类方法相比精度可提高7.86%。专家系统分类的结果叠合居民地、水体等易于混分专题信息,可以形成精度较高的土地利用与覆被分类结果。
New methods have been proposed in this paper to improve the classification accuracy in addition to unsupervised and supervised classification procedures. In land cover classification, only the comparability of pixels is considered, while the figure and texture characteristics are not involved. In contrast, thematic information extraction can make good use of these characteristics and extract classification features more precisely. The authors therefore integrated the land cover classification with the thematic information extraction to improve the classification accuracy. During the study, three methods were selected, namely the expert system classification method for land cover, the decision tree extraction method for residential area and the iterative mixed analytical method for water extraction. These methods were tested in Shaoxing, Jiangning and Nanjing areas respectively. The gross classification accuracy of these new methods is 87.89%, which is about 7.86% higher than the common supervised classification method. Especially in fragmented plots as well as low mountain and hilly areas south of the Yangtze River, the problem of objects classification confusion has been solved effectively.
出处
《国土资源遥感》
CSCD
2004年第4期41-45,i003,共6页
Remote Sensing for Land & Resources
基金
国土资源部重点科技项目"构建国家级土地利用和覆被变化数据库及服务系统(编号2001010101)"资助。
关键词
中分辨率遥感图像
土地利用与覆被
专家系统
决策树
迭代混合分析
Medium resolution remotely sensed data
Land use and cover
Expert system
Decision tree
Iterative mixed analysis
