测绘学报 ›› 2015, Vol. 44 ›› Issue (2): 198-205.doi: 10.11947/j.AGCS.2015.20130453

• 摄影测量学与遥感 • 上一篇    下一篇

视觉感受与Markov随机场相结合的高分辨率遥感影像分割法

许妙忠, 丛铭, 万丽娟, 解天鹏, 朱晓玲   

  1. 武汉大学 测绘遥感信息工程国家重点实验室, 湖北 武汉 430079
  • 收稿日期:2013-12-03 修回日期:2014-06-18 出版日期:2015-02-20 发布日期:2015-02-14
  • 作者简介:许妙忠(1963—),男,教授,博士生导师,主要研究工作是摄影测量与遥感。E-mail:422736042@qq.com
  • 基金资助:
    国家973计划(012CB719900)

A Methodology of Image Segmentation for High Resolution Remote Sensing Image Based on Visual System and Markov Random Field

XU Miaozhong, CONG Ming, WAN Lijuan, XIE Tianpeng, ZHU Xiaoling   

  1. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • Received:2013-12-03 Revised:2014-06-18 Online:2015-02-20 Published:2015-02-14
  • Supported by:
    The National Basic Research Program of China(973 Program)(No.012CB719900)

摘要: 鉴于视觉感受对外界强大的感知与识别能力, 模拟视觉神经感知的工作机制, 并结合Markov随机场模型, 提出一种新的影像分割方法。首先, 分析视觉感知系统的工作机制, 将其特性归纳为等级层次性、学习能力、特征检测能力和稀疏编码特性, 继而利用小波变换、非监督聚类、特征分析和Laplace分布模拟视觉工作机制, 然后结合Markov随机场模型实现高分辨率遥感影像的分割。通过不同卫星的真实遥感影像进行相关试验。试验结果表明本文提出的方法在高分辨率遥感影像分割任务中有非常良好的表现。

关键词: 视觉感知系统, 遥感影像, 小波变换, Markov随机场, 影像分割

Abstract: In consideration of the visual system's tremendous ability to perceive and identify the information, a new image segmentation method is presented which simulates the mechanism of visual system for the high resolution remote sensing image segmentation with Markov random field model. Firstly, the characteristics of the visual system have been summarized as: hierarchy, learning ability, feature detection capability and sparse coding property. Secondly, the working mechanism of visual system is simulated by wavelet transform, unsupervised clustering algorithm, feature analysis and Laplace distribution. Then, the segmentation is achieved by the visual mechanism and the Markov random field. Different satellites remote sensing images are adopted as the experimental data, and the segmentation results demonstrate the proposed method have good performance in high resolution remote sensing images.

Key words: visual system, remote sensing image, wavelet transform, Markov random field, image segmentation

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