自选择混合分布模型的CFAR用于SAR图像舰船检测

The Ship detection of SAR image using CFAR based on mixture models chosen adaptively

  • 摘要: 为了解决恒虚警率检测算法(CFAR检测)在合成孔径雷达图像舰船检测中,用已有分布建模,不能应对所有的场景,对于一些复杂场景建模拟合效果不理想的问题,本文使用一种自选择混合分布的CFAR检测方法:首先,对图像进行预处理,减少目标像素对海杂波的影响;其次,利用学习出来的混合分布模型对预处理后的每一块图像进行建模,计算全局阈值,并根据阈值把图像像素分为目标和背景杂波;然后,为防止漏检,重新对场景像素进行建模、检测,重复此过程直到背景杂波中检测不到目标为止;最后加入后处理,减少虚警的产生。这一方法不仅能得到更好的海杂波模型,同时还能提取舰船的更多细节,实验结果证明了这一方法的有效性。

     

    Abstract: In order to solve the problem of ship detection in SAR image that Constant False Alarm Rate (CFAR) detection algorithm cannot deal with under all scene, and may produce some result not fitting,the paper uses CFAR detector based on finite mixture distributions models chosen adaptively:firstly the input image will be preprocessed to reduce the influence of object pixel to sea clutter; secondly mixture models chosen by studying are used to model each preprocessed image patch and compute the global threshold to get the detection result of the image patch; thirdly the pixels which are not regarded as target at the last step are modeled by mixture distribution to computer detection threshold again until all target pixels in SAR image patch are achieved; finally the paper perform postprocessing for the detection result of each image patch to eliminate false alarm and get true ship target. In order to reduce the false alarm. This method can not only obtain better sea clutter model, but also get more detail of the ship. The experimental result shows that the method of the paper is effective to.

     

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