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Modification of CFAR Algorithm for Oil Spill Detection from SAR Data

机译:SAR数据漏油检测CFAR算法的改进

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摘要

It is very difficult to detect oil spills when the scattering intensity of background clutter is inhomogeneous in synthetic aperture radar (SAR) images. To improve the oil detection capability, we propose a modified constant false alarm rate (CFAR)-based method for the detection of oil spills in SAR images. This proposed method combines edge detection technique and CFAR detection theory to improve the accuracy of oil spills detection. First, we segment the image into the areas of interest (AOIs) by using ratio edge detection. Second, to get a more accurate detection result, an improved Weibull-CFAR detector is applied to these AOIs. Experimental results demonstrate that the modified CFAR algorithm can work more effectively than a global CFAR detector for oil spill detection, especially for the inhomogeneous intensity SAR images. This model can detect the target more effectively, and false alarms can be greatly diminished.
机译:当背景杂波的散射强度在合成孔径雷达(SAR)图像中不均匀时,检测漏油非常困难。为了提高机油检测能力,我们提出了一种基于修正的恒定误报率(CFAR)的SAR图像溢油检测方法。该方法结合了边缘检测技术和CFAR检测理论,提高了漏油检测的准确性。首先,我们使用比率边缘检测将图像分割成感兴趣的区域(AOI)。其次,为了获得更准确的检测结果,将改进的Weibull-CFAR检测器应用于这些AOI。实验结果表明,改进的CFAR算法比全局CFAR检测器在漏油检测方面更有效,特别是对于强度不均匀的SAR图像。该模型可以更有效地检测目标,并且可以大大减少错误警报。

著录项

  • 来源
    《Intelligent automation and soft computing》 |2015年第2期|163-174|共12页
  • 作者单位

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China;

    Chinese Acad Sci, Inst Elect, Beijing, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    CFAR; Oil spill detection; Ratio edge detection; SAR;

    机译:CFAR;漏油检测;比率边缘检测;SAR;

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