基于二叉划分树的多维尺度分析图像分类算法

在二叉划分树的基础上提出一种应用多维尺度分析的合并准则。该算法对高光谱图像分析后建立区域模型，利用多维尺度分析各个区域模型的相似性，移除冗余信息对局部降维，对所得数据关联测量确定其关联性后，进行区域合并，形成二叉划分树的树形结构，利用修剪函数对所得二叉划分树进行修剪，完成分类。实验结果表明，该算法应用于高光谱图像分类具有较好的分类效果。

Multidimensional scaling used for image classification based on binary partition trees

A merging criterion used multidimensional scaling on the basis of binary partition trees algorithm has been proposed. It builds regional models after an analysis on hyperspectral image, uses multidimensional scaling on the similarities of each regional model, reduces dimensions by removing redundant information, determines relevance by association measure on obtained data, and forms a BPT tree structure through regional merging. The pruning function is used to prune this tree structure on classification step. And the experimental conclusion demonstrates that it obtains a better effect for hyperspectral image classification.