%0 Journal Article
%T Knowledge Extraction from GIS Database and its Application in Vegetation Classification<br>地理信息系统支持下的知识获取及其在遥感影像植被分类中的应用研究
%A 刘卫国
%A 龚建华
%J 遥感学报
%D 1998
%I 
%X There are many classification methods for thematic mapping using satellite remote sensing image. The accuracy of classification is limited and unsatisfied.Here,a knowledge-based vegetation classification is presented,which includes two main steps:extracting knowledge and classification based on knowledge. The first step is that the knowledge is extracted by analyzing the relationship between the relative environment factors and each kind of vegetation supported by GIS.The distribution maps of several environment factors, including soil, elevation, temperature, precipitation, are selected and are overlapped respectively with vegetation distribution map of previous period.As far as the soil factor, which can not be described with numerical value, the Bayes theory was introduced to describe the probability of the classification. On the other hand, for the factors including elevation, temperature and precipitation described with numerical value, a·new method was introduced to describe the classification probability. In the end,a vector expression is designed to describe the relationship between each kind of environment factors and each kind of vegetation through statistical method.Thus,the knowledge(the vector expression) is extracted. The second step is that a method based on knowledge derived from above is designed to classify the vegetation for remote sensing image. The difference between this method and other methods is that the knowledge is brought into play as assumed several bands of spectrum. During the classification procedure, the contextual check was made to ascertain whether the pixels had been classified into the vegetation types or not that were ecologically valid for the grid cell being considered. After used the knowledge, the accuracy is increased obviously. To summarize, the classification accuracy was increased about 9 percent using the knowledge-based method presented, compared to the supervised classification method that does not use knowledge.
%K Remote sensing image
%K vegetation classification
%K Quantitative knowledge<br>遥感影像，植被分类，定量化知识，地理信息系统
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=A41A70F4AB56AB1B&jid=F926358B31AC94511E4382C083F7683C&aid=5DC7779D68424859DD4A83C4205871A7&yid=8CAA3A429E3EA654&vid=0B39A22176CE99FB&iid=38B194292C032A66&sid=D9AE183D3F5C3C75&eid=C812B90E96151014&journal_id=1007-4619&journal_name=遥感学报&referenced_num=20&reference_num=0