Ship target detection in synthetic aperture radar (SAR) imagery is of great significance in the field of ocean monitoring. Classical constant false alarm rate (CFAR) detectors and emerging information geometry methods are model-driven essentially, requiring precise modeling of the sea clutter distribution. In the complex and changeable ocean scenes, the performance of these two types of detectors is limited. To solve this problem, a ship target detection algorithm in SAR imagery based on the maximum eigenvalue of the sample covariance matrix is proposed in this letter. Without seeking the distribution model of clutter backgrounds, the difference between the target and the clutter background is fully captured by constructing the sample covariance matrix, and its maximum eigenvalue is utilized as the test statistic. Experimental results on measured SAR images show that the proposed method achieves better detection performance and faster calculation speed compared with the existing typical methods.

中文翻译：

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基于最大特征值检测器的SAR图像船舶目标检测


合成孔径雷达（SAR）图像中的船舶目标检测在海洋监测领域具有重要意义。经典的恒虚警率（CFAR）探测器和新兴的信息几何方法本质上是模型驱动的，需要对海杂波分布进行精确建模。在复杂多变的海洋场景中，这两类探测器的性能受到限制。针对这一问题，本文提出一种基于样本协方差矩阵最大特征值的SAR图像中船舶目标检测算法。无需寻求杂波背景的分布模型，通过构造样本协方差矩阵充分捕获目标与杂波背景之间的差异，并利用其最大特征值作为检验统计量。在实测SAR图像上的实验结果表明，与现有典型方法相比，该方法具有更好的检测性能和更快的计算速度。