摘要
针对目前高分辨率遥感影像道路提取准确性不高的问题,提出一种基于改进最大期望(EM)聚类的遥感影像道路提取算法。首先通过形态学预处理来去除道路上的干扰信息,在此基础上通过改进EM聚类自动确定分割区域数目,完成影像分割,最后结合轮廓检测和灰度直方图阈值化完成遥感影像道路的提取。实验结果表明,所提算法能有效屏蔽道路上的噪声,提取出主干道路信息,该方法的准确率达到90%以上,冗余度较低。
The accuracy of current road extraction algorithms for high resolution remote sensing images is low.Aiming at this problem,a road extraction algorithm for remote sensing images based on the improved expectationmaximization(EM)clustering is proposed.First,the morphological preprocessing is carried out to remove the interference information from the road.Then,the improved EM clustering is applied to determine the number of segmentation regions and segment the images automatically.And the extraction of roads from remote sensing images is finally completed through the contour detection and gray histogram thresholding.Experimental results show that the proposed algorithm can effectively screen the noise on the road and extract the main road information,with high accuracy of over 90% and low redundancy.
作者
张宗军
杨风暴
Zhang Zongjun;Yang Fengbao(College of Information and Communication Engineering,North University of China,Taiyuan,Shanxi 030051,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2020年第6期88-94,共7页
Laser & Optoelectronics Progress
关键词
图像处理
道路提取
数学形态学
最大期望聚类
图像分割
image processing
road extraction
mathematical morphology
expectation-maximization clustering
image segmentation
