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Ship detection method for single-polarization synthetic aperture radar imagery based on target enhancement and nonparametric clutter estimation

机译:基于目标增强和非参数杂波估计的单极化合成孔径雷达图像舰船检测方法

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摘要

Ship detection with synthetic aperture radar (SAR) imagery often confronts severe speckle, heterogeneous regions, and system noise which cause false alarms due to the faint ship-sea contrast. Additionally, false negatives also occur when small vessels with low radar back-scatter are observed. To solve these problems, a new ship detection method based on target enhancement and nonparametric clutter estimation is proposed. The method not only improves the ship-sea contrast for homogeneous and nonhomogeneous images but also adaptively estimates the clutter distribution in the enhanced image, which is crucial for the constant false-alarm rate (CFAR) detector. Subsequently, ships in the SAR image are detected by the proposed two-stage kernel density estimation CFAR (KDE-CFAR) with a low false-alarm rate and high detection probability. Compared with most existing algorithms, the proposed method provides a robust detection capability for both homogeneous and nonhomogeneous SAR images. Experimental results also reveal that the proposed method is an effective method for ship detection in various Radarsat-1 and Envisat ASAR images acquired with different operation modes. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
机译:使用合成孔径雷达(SAR)图像进行船舶检测通常会遇到严重的斑点,异质区域和系统噪声,这些缺陷会由于微弱的船海对比而导致误报。此外,当观察到雷达后向散射低的小型船只时,也会产生假阴性。针对这些问题,提出了一种基于目标增强和非参数杂波估计的船舶检测新方法。该方法不仅可以改善均匀和非均匀图像的船海对比度,而且可以自适应地估计增强图像中的杂波分布,这对于恒定的虚警率(CFAR)检测器至关重要。随后,通过提出的两阶段核密度估计CFAR(KDE-CFAR)以低的虚警率和高的检测概率来检测SAR图像中的船只。与大多数现有算法相比,该方法为均质和非均质SAR图像均提供了强大的检测能力。实验结果还表明,该方法是一种有效的方法,可用于以不同的操作模式采集的各种Radarsat-1和Envisat ASAR图像。 (C)2015年光电仪器工程师协会(SPIE)

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