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Adaptive and fast target detection in high-resolution SAR image

机译:高分辨率SAR图像中的自适应快速目标检测

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In this paper, a new adaptive and fast Constant false alarm rate (CFAR) target detection algorithm based on two level CFAR (TL-CFAR) detectors in high-resolution synthetic aperture radar (SAR) images is proposed. In the first level, the initial mask of targets is obtained by Cell Averaging CFAR (CA-CFAR) detector. In the second level, the precise parameters estimation of CFAR in the local window is implemented by removing those pixels that may belong to the neighboring targets which is identified from the first detector. The problem of high computational complexity of two levels CFAR detector is mitigated by introducing the integral image. Real SAR image data is used to verify the effectiveness of the proposed algorithm, and the results indicate that this algorithm can detect targets fast and precisely.
机译:本文提出了一种基于两级CFAR(TL-CFAR)探测器的新的自适应和快速常量误报(CFAR)目标检测算法在高分辨率合成孔径雷达(SAR)图像中。在第一级别中,目标初始掩模是通过细胞平均CFAR(CA-CFAR)检测器获得的。在第二级中,通过去除可以属于从第一检测器识别的相邻目标的那些像素来实现本地窗口中CFAR的精确参数估计。通过引入积分图像来减轻两个级别CFAR检测器的高计算复杂性的问题。真实的SAR图像数据用于验证所提出的算法的有效性,结果表明该算法可以快速且精确地检测目标。

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