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A Novel Ship Segmentation Method Based on Kurtosis Test in Complex-Valued SAR Imagery

机译:复值SAR图像中基于峰度检验的舰船分割新方法

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Traditional ship segmentation methods in synthetic aperture radar (SAR) imagery are mainly based on the intensity/amplitude information. They cannot take fully advantage of the complex information in SAR imagery. This paper proposes a novel ship segmentation method based on kurtosis test in the complex-valued SAR imagery. It can take benefit of the complex information of the SAR imagery. The segmentation rationale is that sea clutter usually obey a Gaussian distribution while ship targets usually obey a sup-Gaussian distribution. Thus, their kurtosis can be different. Kurtosis is invariant with respect to location shift and positive scale changes. It follows that kurtosis of sea clutter remains approximately constant while the amplitude decreases with the incidence angle increasing. Preliminary experimental results based on Gaofen-3 and Sentinel-1 data show that the proposed method can achieve good performance.
机译:合成孔径雷达(SAR)图像中的传统船舶分割方法主要基于强度/幅度信息。他们无法充分利用SAR图像中的复杂信息。提出了一种基于峰度检验的复杂值SAR图像船舶分割方法。它可以利用SAR图像的复杂信息。分割的理由是,海杂波通常服从高斯分布,而船舶目标通常服从高斯分布。因此,其峰度可以不同。峰态在位置偏移和正尺度变化方面是不变的。因此,随着入射角的增加,海杂波的峰度保持近似恒定,而振幅减小。基于Gaofen-3和Sentinel-1数据的初步实验结果表明,该方法可以取得良好的性能。

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