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首页> 外文期刊>International journal of remote sensing >Change detection in synthetic aperture radar images based on non-local means with ratio similarity measurement
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Change detection in synthetic aperture radar images based on non-local means with ratio similarity measurement

机译:基于非局部均值和比率相似度测量的合成孔径雷达图像中的变化检测

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摘要

Image change detection is of widespread interest due to a large number of applications in diverse disciplines. In this study, a novel change detection approach for synthetic aperture radar (SAR) images based on a non-local means algorithm is proposed. A non-local means technique is introduced to generate a difference image by using complete information from a pair of observed images. To take the characteristics of SAR images into account, a new ratio-based relativity measurement between two speckled SAR image patches based on a ratio distance is proposed. Theoretical analysis indicates that the ratio distance is valid for SAR images. The probability density function of the ratio distance is deduced to map the distance into a relativity value. Furthermore, the ratio distance and the probability density function are both parameter-free. The new non-local means technique is successfully applied to extend the classical mean-ratio detector for SAR image detection. Experimental results on real SAR images show that the proposed approach is robust to speckle noise and effective for the detection of change information between multitemporal SAR images.
机译:由于在不同学科中的大量应用,图像变化检测受到广泛关注。提出了一种基于非局部均值算法的合成孔径雷达图像变化检测方法。引入了非局部均值技术,以通过使用一对观测图像中的完整信息来生成差异图像。考虑到SAR图像的特征,提出了一种基于比率距离的新的基于斑点的两个斑点SAR图像块之间的相对性测量方法。理论分析表明,比例距离对SAR图像有效。推导比率距离的概率密度函数,以将距离映射为相对值。此外,比率距离和概率密度函数均无参数。新的非局部均值技术已成功应用于扩展经典均值比检测器以进行SAR图像检测。在真实SAR图像上的实验结果表明,该方法对斑点噪声具有鲁棒性,对于检测多时相SAR图像之间的变化信息有效。

著录项

  • 来源
    《International journal of remote sensing》 |2014年第22期|7673-7690|共18页
  • 作者

    Su Linzhi; Gong Maoguo; Sun Bo;

  • 作者单位

    Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Xian 710071, Peoples R China;

    Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Xian 710071, Peoples R China;

    Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Xian 710071, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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