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SAR image despeckling by combining saliency map and threshold selection

机译:结合显着图和阈值选择进行SAR图像去斑

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摘要

Speckle suppression and detail preservation are a unity of contradiction during synthetic aperture radar (SAR) despeckling. In this article, an effective despeckling method is proposed based on a self-adaptive neighbourhood, which takes into account the trade-off between homogeneous region suppression and point target preservation. The size and shape of the neighbourhood depend on an adaptive threshold that is treated as a linear function of a saliency map and a threshold range estimated by the Monte Carlo algorithm based on a given significance level and the number of looks in SAR. If the saliency of the current pixel is large, which always means that the current pixels should belong to a point target, a linear object, or an edge, a relatively large threshold will be assigned to search a neighbourhood with a small size. Conversely, pixels with a small saliency value will obtain a non-local neighbourhood with a large size. A maximum-likelihood estimation is employed to estimate the real radar reflectivity based on the pixels in the adaptive neighbourhood. The comparison experiments on simulated and actual SAR data validate the effectiveness of the proposed algorithm and demonstrate its overall speckle filtering characteristics compared with other algorithms. The visual and numerical experimental results show that the proposed despeckling method provides superior performance compared with several state-of-the-art despeckling methods.
机译:在合成孔径雷达(SAR)去斑点期间,斑点抑制和细节保留是矛盾的统一体。本文提出了一种基于自适应邻域的有效去斑方法,该方法考虑了均质区域抑制与点目标保持之间的权衡。邻域的大小和形状取决于自适应阈值,该阈值被视为显着性图的线性函数,并且基于给定的显着性水平和SAR中的外观次数,由Monte Carlo算法估算出的阈值范围。如果当前像素的显着性较大,这始终意味着当前像素应属于点目标,线性对象或边缘,则将分配相对较大的阈值以搜索较小的邻域。相反,显着性值小的像素将获得大尺寸的非局部邻域。基于自适应邻域中的像素,采用最大似然估计来估计实际雷达反射率。在模拟和实际SAR数据上进行的对比实验验证了该算法的有效性,并与其他算法相比证明了其整体斑点滤波特性。视觉和数值实验结果表明,与几种最新的去斑方法相比,该方法具有更好的性能。

著录项

  • 来源
    《International journal of remote sensing》 |2013年第22期|7854-7873|共20页
  • 作者单位

    Intelligent Perception and Image Understanding Key Lab of Ministry of Education of China,Institute of Intelligent Information Processing, Xidian University, Xi 'an 710071, China;

    Intelligent Perception and Image Understanding Key Lab of Ministry of Education of China,Institute of Intelligent Information Processing, Xidian University, Xi 'an 710071, China;

    Intelligent Perception and Image Understanding Key Lab of Ministry of Education of China,Institute of Intelligent Information Processing, Xidian University, Xi 'an 710071, China;

    Intelligent Perception and Image Understanding Key Lab of Ministry of Education of China,Institute of Intelligent Information Processing, Xidian University, Xi 'an 710071, 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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