基于K-L变换的BP神经网络遥感图像分类

为了提高多光谱遥感图像的分类正确,提出了一种基于主成分分析(K-L变换)的分类方法。该方法先应用K-L变换对多波段遥感图像进行降维,提取最主要的三个成分合成假彩色图,然后利用BP神经网络对假彩色图进行监督分类。由于主成分之间是不相关的,增强了图象信息,降低了神经网络的计算量,提高了分类精度。实验结果证明,该算法分类精度优于传统分类方法,总正确率为88.5%,Kappa系数为0.862,因而具有实用价值。

BP neural network based on principle component analysis in multi-spectral remote sensing images classification

In order to improve the classification accuracy of multi-spectral remote sensing image,this paper puts forward a new classification method based on principal component analysis.The method is consisted of two steps: reducing the dimensions of multi-spectral remote sensing image with principle component analysis and generating a new image by the three main components of the remote sensing image;performing supervised classification on the new image with BP neural network.The result indicates that this method i...