优化区域生长的高分辨率影像道路精确提取  

Precise Road Extraction of High-resolution Imagery with Optimized Region Growing

作　　者：陶思然 陈性义[1] 张海涛[1,2] Tao Siran;Chen Xingyi;Zhang Haitao(Faculty of Information Engineering, China University of Geosciences , Wuhan 430074,P. R. China;Faculty of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450000,P. R. China)

机构地区：[1]中国地质大学(武汉)信息工程学院,武汉430074 [2]河南财经政法大学资源与环境学院,郑州450000

出　　处：《科学技术与工程》2017年第33期136-143,共8页Science Technology and Engineering

基　　金：国家863计划资助项目(2007AA092102);高分辨率对地观测重大专项(07-Y30A05-9001-12/13)资助

摘　　要：鉴于区域生长存在需提供种子点及易过生长的缺陷,改进了区域生长算法对高分辨率遥感影像进行道路精确提取。首先利用格网对原始影像进行划分,将在道路灰度范围内的外边缘格网以同质性指标依次排序作为候选种子点;将满足道路连续性和一致性要求的路径格网起始点作为种子点;并通过道路指数加以验证得到最终种子点,以此避免盲目选择种子点的不足。其次在引入梯度值进行道路生长后,以二值化的彩色分量与生长结果进行信息融合方式来优化算法,达到缓解过生长的目的。实验结果表明算法是合理有效的,能够较为精确地提取出道路区。In view of tiie region growing witii defects of being provided seed points and overgrowth,the regional growth algorithm is improved to extract high-resolution remote sensing images accurately.Firstly,the original imageis divided by the grid,and the outer edge grids in the gray scale of the road are sorted by the the homogeneity index as the candidate seed points.The starting point of the path grid can meet the requirements of continuity and consistency of the road is the seed points,and it is verified by road index to get tiie final seed points,so as to avoid shortage of blind selection of seed points.Secondly,after the introduction of the gradient value for road growth,the binarized color component and the growth result are used to optimize the algorithm to achieve the purpose of alleviating the growth.The experimental results show that the algorithms are reasonable and effective.And the algoritiims can extract tiie road area more accurately.

关 键 词：种子点自动获取 梯度值 区域生长 彩色分量 高分辨率道路影像 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]