提出了基于案例推理 (CBR)的遥感分类的新方法。基于规则的专家系统被用来提高遥感分类的效率。但所涉及的规则可能多达上百或上千条 ,有的问题根本无法用规则来表达。CBR只是根据以往的案例进行推理 ,克服了基于规则的推理方法的不足 ,可以用来解决一些复杂的资源环境问题。利用CBR和模糊数学结合 ,并通过分层随机采样来控制案例在空间上的分布 ,以反映复杂环境所造成的光谱变化 ,由此解决遥感分类中的“同物异谱”现象。实验表明 ,所建立的案例库可以被重复多次使用 ,分类的效果比监督分类和非监督分类要好。

This paper presents a new method for classifying satellite SAR images based on case based reasoning (CBR) techniques. Because classification is a common task in remote sensing applications, numerous methods have been deve loped for obtaining better classification results. Knowledge based systems (KBS) are considered as a good alternative to traditional classification methods with better performance. There is a need to develop such systems to facilitate the interpretation of remote sensing data in a more efficient way. KBS are useful when concrete knowledge about the application domain is available. It is expected that KBS can automatically classify remote sensing images without operator's intervention. However, these systems have a bottleneck problem in the solicitation of rules. A solution is to apply CBR method to the classification process. Traditional classification often assumes that spectral properties of a class remain stable in the whole study area. However, the spectral signature of a class is usually subject to fluctuations because of the complexity in nature. The CBR method can easily capture such fluctuations by allocating cases over different terrain features according to stratified random sampling. Moreover, the same case library developed in the previous classification can be reused for time independent classification with satisfactory results. Experiments show that the proposed method can generate the classification results with better performance in term of higher accuracy and fast computation time. The method has been successfully applied to the classification of radar SAR images in the Pearl River Delta, south China.