一种新的基于Dempster-Shafer理论的自适应遥感分类融合方法

A NEW ADAPTIVE CLASSIFICATION FUSION METHOD BASED ON DEMPSTER-SHAFER THEORY IN REMOTE SENSING IMAGE

提出了一种基于Dempster-Shafer’s理论和模糊Kohonen神经网络分类融合的方法。该方法融合了非监督神经网络模型和在Dempster-Shafer证据理论框架中使用邻域信息的思想 ,即当一个待识别模式的每个邻域被划分为支持识别框架中某一类的一个证据体时 ,该证据体支持关于该模式隶属关系的某一假设。SPOT遥感数据的分类实验证明 ,该方法同已有的神经网络技术分类方法相比较 。

In this paper, a new adaptive classification fusion method was proposed based on the Dempster-Shafer's theory of evidence and fuzzy Kohonen neural network in remote sensing image. The new method incorporates ideas from unsupervised neural network model and uses neighborhood information in the framework of the Dempster-Shafer theory of evidence. This approach mainly consists in considering each neighbor of a pattern to be classified as an item of evidence supporting certain hypotheses concerning the class membership of that pattern. This evidence is represented by basic probability assignment, with pooled utilization of the Dempster's rule of combination. Experiments with SPOT remote sensing image demonstrate the excellent performance of this classification scheme as compared with the existing neural network techniques.