数字挖掘方法在遥感分类中的应用研究

随着空间信息领域技术的提高,利用卫星遥感数据获取地表信息的数据量也飞速的增长,快速有效地在海量遥感数据源中获取感兴趣的地表信息成为一项重要的研究方向。数据挖掘方法具有从海量数据集中提取隐含其中信息的功能,使得数据挖掘方法在遥感图像分类和专题信息提取中具有较好的应用,但不同的数据挖掘算法具有各自的独特性,使得在遥感分类中使用数据挖掘方法并不容易,本文通过介绍几种常用的数据挖掘算法,分析和探讨了这些方法在遥感分类应用中的优势和局限性,为在遥感分类中更好的、有针对性的选择数据挖掘算法提供借鉴。

Application of Data Mining Methods in Remote Sensing Classification

With the improvement of spatial information technology,the data of earth surface information obtained by satellite Remote Sensing technology has increased quickly. Efficiently and quickly obtain earth surface information in the mass of remote sensing data sets has become an important research direction. Data mining methods have the function of extracting useful information from the mass of data sets,and have the better application in classification of remote sensing images. It is not easy to classify remote sensing images using data mining methods because each type of data mining method has unique characteristics. This paper introduces some commonly used data mining algorithms,then analyzes and discusses the advantages and limitations of these algorithms in remote sensing classification. The research will help to select the best data mining method in remote sensing classification.