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Region based target detection approach for synthetic aperture radar images and its parallel implementation

机译:基于区域的合成孔径雷达图像目标检测方法及其并行实现

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摘要

Automatic target detection (ATD) methods for synthetic aperture radar (SAR) imagery are sensitive to image resolution, target size, clutter complexity, and speckle noise level. However, a robust ATD method needs to be less sensitive to the above factors. In this study, a constant false alarm rate (CFAR) based method is proposed which can perform target detection independent of image resolution and target size even in heterogeneous background clutter. The proposed method is computationally efficient since clutter statistics are calculated only for candidate target regions and a single execution of the method is sufficient for different types of targets having different shapes and sizes. Computational efficiency is further increased by parallelizing the algorithm using OpenMP and NVidia CUDA implementations.
机译:合成孔径雷达(SAR)图像的自动目标检测(ATD)方法对图像分辨率,目标尺寸,杂波复杂度和斑点噪声水平很敏感。但是,鲁棒的ATD方法需要对上述因素不那么敏感。在这项研究中,提出了一种基于恒定虚警率(CFAR)的方法,即使在异构背景杂波中,该方法也可以执行独立于图像分辨率和目标尺寸的目标检测。所提出的方法在计算上是有效的,因为仅针对候选目标区域计算杂波统计,并且该方法的单次执行对于具有不同形状和大小的不同类型的目标就足够了。通过使用OpenMP和NVidia CUDA实现并行化算法,可进一步提高计算效率。

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