核概率距离聚类方法及应用  

Method and Application of Kernel Probabilistic Distance Clustering

作　　者：陈永良[1] 李学斌[1] 

机构地区：[1]吉林大学综合信息矿产预测研究所,长春130026

出　　处：《吉林大学学报（地球科学版）》2013年第1期312-318,共7页Journal of Jilin University：Earth Science Edition

基　　金：国家自然科学基金项目(40872193;41072244;41272360);国家自然科学基金重点项目(61133011)

摘　　要：研究聚类分析新方法一直是统计学和机器学习研究领域普遍关注的课题。针对概率距离聚类算法不能解决非线性可分聚类问题的缺欠,笔者应用核函数理论将该模型拓展成为一种能够解决非线性可分聚类问题的统计模型,称为核概率距离聚类分析模型。研制出一种应用新模型进行遥感图像非监督分类研究的实施策略和可行算法;在GDAL遥感图像数据输入输出函数库基础上,用VC++语言开发了遥感图像核概率距离聚类分析算法程序;用ERDAS软件提供的一幅7波段491像素×440像素大小的TM图像进行新方法分类应用实验研究。对比了新模型和其原版本的TM遥感图像非监督分类效果,结果表明新模型的非监督分类效果优于原有的分类模型。Investigating a new clustering methods is always paid more attention by the researchers in statistics and machine learning fields. Noticing that probabilistic distance clustering cannot solve nonlinear clustering problems, the authors proposed a new nonlinear clustering method called kernel probabilistic distance clustering by extending probabilistic distance clustering algorithm with kernel function theories, and then laid out an executing strategy and feasible algorithm of the new model for the unsupervised classification of remotely sensed image data, and finally developed a visual C＋ ＋ program of the new model for remotely sensed data classification on the basis of GDAL function library for the input and output of remotely sensed data. A TM image with seven bands and 491 X 440 pixels in size provided by ERDAS software platform was applied to the experimental application of the new model for unsupervised classification. The authors compared the clustering performances of the new model and its original version in the unsupervised classification of the TM images. The experimental results show that the clustering performance of new model is better than that of the original linear version in unsupervised classification.

关 键 词：概率距离聚类 核函数 核概率距离聚类 遥感图像 非监督分类 

分 类 号：P628.1[天文地球—地质矿产勘探]