一种改进的高分辨率SAR图像超像素CFAR舰船检测算法

合成孔径雷达(SAR)图像舰船目标检测一直受到学者广泛关注，恒虚警率(CFAR)检测算法作为雷达图像经典目标检测算法被广泛应用于SAR图像舰船目标检测中。然而经典CFAR检测性能容易受到相干斑噪声影响，基于滑窗的检测结果对滑窗的尺寸选择非常敏感，难以保证杂波背景中不存在目标像素，并且计算效率较低。针对上述问题，该文提出了一种新的基于超像素无窗快速CFAR的SAR图像舰船目标检测算法。首先，利用基于密度的快速噪声空间聚类(DBSCAN)超像素生成方法生成SAR图像的超像素。在SAR数据服从混合瑞利分布的假设下，定义了超像素相异度。然后利用超像素精确估计每个像素的杂波参数，即使在多目标情况下，也可以克服传统CFAR滑动窗口的缺点。此外，基于SAR图像变异系数，提出了一种基于变异系数的局部超像素对比度来优化CFAR检测，以此消除大量杂波虚警，如陆地区域人造目标。对5幅SAR图像的实验结果表明，与其他方法相比，该文方法对不同场景SAR图像海面舰船目标检测都十分稳健。 

An Improved Superpixel-based CFAR Method for High-resolution SAR Image Ship Target Detection

Synthetic Aperture Radar (SAR) image ship target detection has attracted considerable attention. As a state-of-the-art method, the Constant False Alarm Rate (CFAR) detection algorithm is often used in SAR image ship target detection. However, the detection performance of the classical CFAR is easily affected by speckle noise. Moreover, the detection results based on the sliding window are sensitive to the size of the sliding window. Thus, ensuring that there are no target pixels in the cluttered background is difficult, which easily leads to a high computational load. This study proposes a new ship target detection method for SAR images based on fast superpixel-based non-window CFAR to solve these problems. The superpixel generation method of Density Based Spatial Clustering of Applications with Noise is used to generate superpixels for SAR images. Under the assumption that SAR data obey the Rayleigh mixture distribution, we define a superpixel dissimilarity measure. Then, the clutter parameters of each pixel are accurately estimated using superpixels, which can avoid the shortcomings of the traditional CFAR sliding window even in the case of multiple targets. A local contrast based on the Coefficient of Variation (CoV) of the SAR image is proposed to optimize the CFAR detection result, which can eliminate a large number of false alarms from man-made targets in urban areas. The experimental results of five real SAR images show that the proposed method for ship target detection in SAR images with different scenes is robust compared with other state-of-the-art methods.