中分辨率遥感图像土地利用与覆被分类的方法及精度评价

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。

METHODS AND ACCURACY ASSESSMENT FOR LAND USE AND COVER CLASSIFICATION BASED ON MEDIUM RESOLUTION REMOTELY SENSED DATA

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.