地理信息系统支持下的山区遥感影像决策树分类

GIS SUPPORTED DECISION TREE CLASSIFICATION OF REMOTE SENSING IMAGES IN MOUNTAINOUS AREAS

山区遥感影像分类是遥感研究的一大难题。本文利用一种决策树生成算法(C 4.5算法)自动提取知识,基于知识建立决策树用于山区影像分类,并结合研究区土地利用类型与DEM空间统计关系的先验知识,在G IS空间分析的基础上进行影像分类的后处理。与传统的最大似然法分类结果相比,该方法极大地改善了山区地表覆被分类的精度,得到试验区较为可靠的遥感分类图像。

Remotely sensed data based land use/cover classification, especially in mountainous areas, is a difficult problem that has long drawn attentions among researchers. This paper presents a synthetic approach using CA. 5 algorithm to automatically derive classification knowledge with the purpose of constructing a model of decision tree for the final classification of the image. Statistical relationships of the land - use pattern with DEM were analyzed through spatial analysis function of GIS to provide extra knowledge for the post classification processes, which improves the precision of final classification by enhancing the characteristics of the trial zones in the image. According to a classification experiment on the rugged terrain over the upstream of Hanjiang River Basin where the land use/ cover ground survey data are available, the proposed approach is far superior to the traditional maximum likelihood classification method.