核聚类改进的RBF神经网络遥感影像分类  

Improved RBF neural network RS image classification algorithm based on kernel clustering

作　　者：王梦秋[1] 万幼川[1] 李刚[1] 

机构地区：[1]武汉大学遥感信息工程学院,武汉430079

出　　处：《测绘科学》2014年第1期96-100,共5页Science of Surveying and Mapping

基　　金：国家支撑项目(2012BAH34B02;2012BAJ15B04;2011BAH12B03)

摘　　要：为提高RBF神经网络遥感影像分类算法的分类效果,本文提出了一种基于核聚类的改进方法。文中采用武汉地区的SPOT影像为实验数据,比较了改进算法与传统基于K均值的RBF神经网络的分类结果,试验表明改进算法的总体精度和Kappa系数均高于传统算法。The purpose of this article is to enhance the classification performance of RBF neural net- work on remote sensing images by applying an improved algorithm based on kernel clustering. An experi- merit was conducted on：the SPOT image of Wuhan. Comparison of the classification results between the imprdved algorithm with the traditional RBF neural network indicated that the improved method has a higher overall accuracy and Kappa coefficient than the traditional method.

关 键 词：遥感影像分类 RBF神经网络 核聚类 OTSU算法 影像分割 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置] P237.9[自动化与计算机技术—控制科学与工程]