华南理工大学学报(自然科学版) ›› 2017, Vol. 45 ›› Issue (3): 29-34.doi: 10.3969/j.issn.1000-565X.2017.03.004

• 电子、通信与自动控制 • 上一篇    下一篇

用于数字表面模型建筑物分割的LS -ORTSEG 方法

闫奕名 赵春晖 崔颖   

  1. 哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨 150001
  • 收稿日期:2016-05-25 修回日期:2016-09-06 出版日期:2017-03-25 发布日期:2017-02-02
  • 通信作者: 闫奕名( 1982-) ,男,博士,讲师,主要从事遥感图像处理研究. E-mail:yanyiming@hrbeu.edu.cn
  • 作者简介:闫奕名( 1982-) ,男,博士,讲师,主要从事遥感图像处理研究.
  • 基金资助:
    国家自然科学基金青年科学基金资助项目( 61601135) ; 黑龙江省自然科学基金重点资助项目( ZD201216)

LS-ORTSEG Method for Segmentation of Buildings in Digital Surface Model

YAN Yi-ming ZHAO Chun-hui CUI Ying   

  1. College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,Heilongjiang,China
  • Received:2016-05-25 Revised:2016-09-06 Online:2017-03-25 Published:2017-02-02
  • Contact: 闫奕名( 1982-) ,男,博士,讲师,主要从事遥感图像处理研究. E-mail:yanyiming@hrbeu.edu.cn
  • About author:闫奕名( 1982-) ,男,博士,讲师,主要从事遥感图像处理研究.
  • Supported by:
    Supported by the National Natural Science Foundation of China for Young Scientists( 61601135) and the Key Program of the Natural Science Foundation of Heilongjiang Province of China( ZD201216)

摘要: 基于数字表面模型( DSM) 的建筑物分割是遥感三维城市建模的关键技术之一.为解决DSM 分割中因地形起伏和边界处干扰物等引起的建筑物分割精度低的问题,文中
提出一种层次化的建筑物分割方法LS-ORTSEG. 该方法首先利用水平集方法初步提取各个潜在的建筑物区域,对各潜在区域进行适当扩展,进而针对扩展区域利用一种基于多重随机纹理模型的分割方法进行精细分割,进一步优化建筑物局部边界分割结果. 实验表明,文中方法能够有效提高建筑物分割精度.

关键词: 数字表面模型, 建筑物分割, 水平集, 多重随机纹理模型

Abstract:

Segmenting buildings in digital surface model ( DSM) is a key technique of 3D city reconstruction based on remote sensing data.In order to overcome the poor performance caused by the negative interference of topographic relief and edge in the segmentation of buildings in DSM,a hierarchical segmentation method named LSORTSEG is proposed in light of both level-set ( LS) method and the model of occlusions of random texture ( ORTSEG) .In this method,firstly,the potential building regions are extracted roughly by means of the LS method.Secondly,the regions are expanded properly.Then,the expanded regions are segmented by using the ORTSEG for an optimized segmentation of buildings.Experimental results show that the proposed method can improve the segmentation accuracy of buildings effectively.

Key words: digital surface model, building segmentation, level-set, multiple random texture model

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