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山东大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 1-9.doi: 10.6040/j.issn.1672-3961.0.2017.406

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基于正反向异质性的遥感图像变化检测

李士进,王声特,黄乐平   

  1. 河海大学计算机与信息学院, 江苏 南京 210098
  • 收稿日期:2017-05-09 出版日期:2018-06-20 发布日期:2017-05-09
  • 作者简介:李士进(1973— ),男,江苏泰州人,教授,博士,主要研究方向为模式识别与计算机视觉. E-mail:lishijin@hhu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61170200);江苏省重点研发计划(社会发展)资助项目(BE2015707)

Change detection with remote sensing images based on forward-backward heterogenicity

LI Shijin, WANG Shengte, HUANG Leping   

  1. College of Computer and Information, Hohai University, Nanjing 210098, Jiangsu, China
  • Received:2017-05-09 Online:2018-06-20 Published:2017-05-09

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摘要： 为提高水体周边环境的变化检测结果的精度,提出一种改进的变化检测方法。在光谱与纹理特征结合的基础上融合指数特征构建混合特征空间,采用超像素生成算法(simple linear iterative cluster, SLIC)处理叠加影像获取地物对象,并综合地物对象的正反向异质信息构建地物对象的正反向异质性;使用最大数学期望算法与贝叶斯最小错误率理论获取两时相的变化信息,排除植被伪变化信息,形成相对准确和鲁棒的检测结果。试验结果表明:该方法能够有效区分水体周边环境中感兴趣的地物变化信息与不感兴趣的干扰信息、“伪变化信息”等,虚检率和漏检率较低,且正确率较高为96%以上,能够智能发现湖库水域周边“非正常”土地利用变化。

关键词: 水体, 变化检测, 正反向异质性, 超像素生成算法, 混合特征空间

Abstract: In order to improve the change detection accuracy of water surrounding environment, an improved change detection method was proposed. This method was based on the combination of spectral and textural features, and fused the index feature to construct a hybrid feature space. The simple linear iterative cluster(SLIC)algorithm was used to obtain ground objects by processing the superimposed images. Meanwhile, the proposed method synthesized various forward-backward heterogeneity information to construct the forward-backward heterogeneity of ground objects. The EM algorithm and the minimum error Bayes decision theory were used to obtain the change information of the images on two phases. By eliminating the pseudo change information of vegetation, the relative robust and exact detection results could be achieved. Experimental results showed that the proposed method could effectively distinguish the useful change information from uninterested disturbance information and pseudo change information, and had low false detection ratio and low missing detection ratio. The accuracy of the proposed method could reach more than 96%. Moreover, this method could intelligently recognize the abnormal land-use changes around lakes and reservoirs.

Key words: water body, simple linear iterative cluster, hybrid feature space, forward-backward heterogenicity, change detection

中图分类号: 

  • TP753
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