测绘学报

• 学术论文 • 上一篇    下一篇

MRF框架下的区域增长模型在城镇识别中的应用

陈荣元1,2,郑晨2,王雷光2,3,秦前清4   

  1. 1. 湖南商学院
    2. 武汉大学
    3. 西南林学院
    4. 武汉大学测绘遥感国家重点实验室
  • 收稿日期:2010-02-03 修回日期:2010-09-16 出版日期:2011-04-25 发布日期:2011-04-25
  • 通讯作者: 秦前清

A Region Growing Model under the Framework of MRF for Urban Detection

  • Received:2010-02-03 Revised:2010-09-16 Online:2011-04-25 Published:2011-04-25

摘要: 提出一种MRF框架下以过分割区域为基本生长单位的区域增长模型,并以其实现城镇识别。该模型首先通过纹理分析和滤波运算得到初始种子点;然后由均值漂移算法运算过分割区域,并将种子点对应的区域设为种子区域;最后,从种子区域开始,根据MRF框架下提出的增长准则,得到最终的城镇识别结果。对QuickBird和Ikonos遥感影像的实验表明,该模型能有效地识别出影像中的城镇区域,城镇平均识别率达到84.35%,平均识别正确率为93.16%。

Abstract: A region growing model under the framework of MRF is proposed for urban detection and the basic unit of the model is the over segmentation region. Firstly, the texture analysis is employed to obtain the initial seed points. Then Mean Shift algorithm is employed to get the over segmentation and the regions that include seed points are used as seed regions. At last, starting from the seed regions, the finally result of urban is detected through a growing criterion under the framework of MRF. The experiments of QuickBird and Ikonos demonstrate that the model can effectively detect the urban area from the remote sensing images. The average ratio of urban detection is 84.35% and the average ratio of correct detection is 93.16%.