摘要
在分析了基于光谱特征的统计模式识别方法用于遥感图像的计算机分类的不足之后，探讨了进一步提高遥感图像分类结果精度及可靠性的途径，指出了在遥感图像的计算机自动分类过程中，综合利用遥感图像多光谱特征及光谱特征以外的辅助信息对遥感图像进行分类是解决上述问题的有效方法，并通过笔者研制的草场资源分类专家系统ＧＥＳ（ＧｒａｓｓｌａｎｄＲｅｓｏｕｒｃｅｓＣｌａｓｓｉｆｉｃａｔｉｏｎＥｘｐｅｒｔＳｙｓｔｅｍ）说明了专家系统技术用于遥感图像分类能够有效地解决分类过程中综合利用各种辅助信息的问题。
Remote sensing daal is widely used in earth resources investigation and has madea great progress. Unfortunately,because the computer classfication is based on multispectralfeatures of adages,the accuracy and realiablity are licitted. To improve the accuracy and realisblity of recognizable classes of sround objectS,many People have been strivins for a longtime and many methods have been prompted. A better way is to simultaneously use manykinds of auxiliary data, such as DTM,geographic features soil typeS, climate, relief, verticaland regional distribution etc.,with remote sensing image in computer classification. For human interpreter,it is not a very difficult problem. But it is not easy for computer. At the inerment there is a tendency to develop Expert System in remote senSinS image computer classification. It is an efficient way to solve this problem.The structure of Expert SyStem that uses TM image for gtass resources classification isdescribed. Two portS are included in GES:1)Hish level processins pert. This port includes the following modules. Knowledsehose, Inference engine, Database, Nature language module, Explain module, Knowledge acquisition module etc.. For inference engine,grey inference theory is introduced to inexactinginference.2)Low level processins pert. This port is deSisned to extract information from imageand auxiliary daal. Such as DTM,soil typeS, teXture features etc.. The image seSmantutionand output of claSSification resultS are also completed by this part.
出处
《武汉测绘科技大学学报》
CSCD
1994年第1期45-51,共7页
Geomatics and Information Science of Wuhan University
关键词
专家系统
草场资源
分类
expert system
auxiliary data
reverse matching
image classification
grass resource
