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Superpixel-Level CFAR Detectors for Ship Detection in SAR Imagery

机译:SAR图像中用于舰船检测的超像素级CFAR检测器

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摘要

Synthetic aperture radar (SAR) is one of the most widely employed remote sensing modalities for large-scale monitoring of maritime activity. Ship detection in SAR images is a challenging task due to inherent speckle, discernible sea clutter, and the little exploitable shape information the targets present. Constant false alarm rate (CFAR) detectors, utilizing various sea clutter statistical models and thresholding schemes, are near ubiquitous in the literature. Very few of the proposed CFAR variants deviate from the classical CFAR topology; this letter proposes a modified topology, utilizing superpixels (SPs)nin lieunof rectangular sliding windows to define CFAR guardbands and background. The aim is to achieve better target exclusion from the background band and reduced false detections. The performance of this modified SP-CFAR algorithm is demonstrated on TerraSAR-X and SENTINEL-1 images, achieving superior results in comparison to classical CFAR for various background distributions.
机译:合成孔径雷达(SAR)是用于大规模监测海上活动的最广泛使用的遥感方式之一。由于固有的斑点,可识别的海杂波以及目标物几乎没有可利用的形状信息,因此在SAR图像中进行船舶检测是一项具有挑战性的任务。恒定的误报率(CFAR)检测器,利用各种海杂波统计模型和阈值方案,在文献中几乎无处不在。提出的CFAR变体中很少有偏离经典CFAR拓扑的;这封信提出了一种改进的拓扑,利用超像素(SP)n <斜体xmlns:mml =“ http://www.w3.org/1998/Math/MathML” xmlns:xlink =“ http://www.w3.org / 1999 / xlink“>代替矩形滑动窗口来定义CFAR保护带和背景。目的是实现从背景带更好地排除目标并减少错误检测。在TerraSAR-X和SENTINEL-1图像上演示了此改进的SP-CFAR算法的性能,与各种背景分布下的经典CFAR相比,其效果都更好。

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