遥感图像中多分类问题的树型RBF神经网络方法

该文探讨RBF映射理论在遥感影像分类中的具体算法和实现过程，给出了基于自适应聚类间距的快速聚类算法(AGDFC)的RBF网络训练算法和树型RBF网络构造算法。然后以实际的遥感土地覆盖分类为例，通过与最大似然分类算法(MLC)相比较，对分类过程和结果进行了综合分析，实验结果表明树型RBF网络方法在学习速度、网络结构、分类精度等方面具有一定的优势。

RBF neural tree networks for multi-class classification in remote sensing

In this paper, the algorithm and realizing procedures of the RBFNN used in classification of remote sensing image were discussed, and a training algorithm based on Adaptive Global Distance Fast Cluster (AGDFC) and a tree-like hierarchical RBFNN constructing algorithm were. Then, the case of practical application of remote sensing land cover classification in Tai'an region was presented. Through comparing with MLC, classification process and results were synthetically analyzed. Experimental results show that RBF neural tree networks approach has more advantages in training time, network structure, classification precision, etc.