应用科学学报 ›› 2012, Vol. 30 ›› Issue (2): 173-180.doi: 10.3969/j.issn.0255-8297.2012.02.011

• 论文 • 上一篇    下一篇

应用启发式边缘生长的遥感影像分割

李刚1, 万幼川1, 李萌2   

  1. 1. 武汉大学遥感信息工程学院,武汉430079
    2. 滑铁卢大学地理与环境管理学院,加拿大N2L3G1
  • 收稿日期:2011-05-24 修回日期:2011-08-31 出版日期:2012-03-26 发布日期:2012-03-30
  • 通信作者: 李刚,博士,研究方向:遥感图像处理、模式识别,E-mail: whulg@163.com
  • 作者简介:李刚,博士,研究方向:遥感图像处理、模式识别,E-mail: whulg@163.com;万幼川,教授,博导,研究方向:遥感与信息系统、数字城市、数字流域,E-mail:ycwan@whu.edu.cn
  • 基金资助:

    国家科技支撑计划项目基金(No.2011BAH12B03)资助

Remote Sensing Image Segmentation Based on Heuristic Edge Growing

LI Gang1, WAN You-chuan1, LI Meng2   

  1. 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
    2. Department of Geography and Environmental Management, University of Waterloo, Ontario N2L3G1, Canada
  • Received:2011-05-24 Revised:2011-08-31 Online:2012-03-26 Published:2012-03-30

摘要:

 基于边缘的遥感影像分割方法有两个难点:边缘点检测和边缘线连接. 文中提出一种基于启发式边缘生长的分割方法. 首先对Canny 算子进行三方面的改进以准确提取边缘点:自适应小波去噪、最优双阈值计算、基于邻域全变分的边缘决策. 定义一个新的边缘连接异质性指标,包括空间异质性和光谱异质性. 在此基础上提出启发式的全局交互最优决策技术以正确连接断裂边缘线. 文中用快鸟影像和航空影像进行分割实验,并与eCognition 的分割结果进行定性和定量比较. 实验表明启发式边缘生长分割方法能正确地连接绝大多数边缘线,并提供准确的分割结果.

关键词: 影像分割, 启发式边缘生长, 自适应边缘检测, 邻域全变分, 小波去噪

Abstract:

In view of difficulties of edge point detection and edge line linking in edge-based segmentation,this paper proposes a new segmentation method for remote sensing images based on heuristic edge growing.First, improvements are made to the Canny operator for edge point detection including adaptive wavelet de-noising, optimal double-threshold calculation and edge point decision based on total variation. A new heterogeneity index of edge linking is defined, which includes spatial heterogeneity and spectral heterogeneity.Heuristic decision of global mutual best fitting is then proposed to link broken edges of the same target as far as possible. Experiments are carried out using Quickbird image and aerial image to evaluate the proposed method. Segmentation results are compared to those obtained with eCognition, showing that the proposed method can link most edges correctly and produce accurate segmentation results.

Key words: image segmentation, heuristic edge growing, adaptive edge detection, total variation, wavelet de-noising

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