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A Variational Level Set Approach for Automatic Target Extraction of SAR Images

机译:SAR图像目标自动提取的可变水平集方法

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A variational level set approach for automatic target extraction of SAR images is presented in this paper. Target extraction of SAR image is a hard work due to the presence of speckle noise. The proposed approach defines an energy criterion that consists of a region term derived from a mixed Gamma model of speckle noise and a boundary term related to the image gradient. Both of the terms are directly defined on the level set function. It is obviously different from the energy functional defined on parameterized curve in early level set approach. Target extraction is implemented by minimizing the energy criterion via variational level set approach. The performance of the approach is verified by MSTAR data. The experimental results show that the approach takes account of the speckle noise effect and sufficiently uses the boundary information of SAR images, thus accurately extracts the target but does not need speckle preprocessing step.
机译:本文提出了一种用于SAR图像自动目标提取的变分水平集方法。由于斑点噪声的存在,SAR图像的目标提取是一项艰巨的工作。所提出的方法定义了一种能量准则,该能量准则由散斑噪声的混合Gamma模型得出的区域项和与图像梯度有关的边界项组成。这两个术语都直接在级别设置功能上定义。它明显不同于早期水平集方法中参数化曲线上定义的能量函数。目标提取是通过使用可变水平集方法最小化能量标准来实现的。 MSTAR数据验证了该方法的性能。实验结果表明,该方法考虑了斑点噪声的影响,充分利用了SAR图像的边界信息,可以准确地提取出目标,但不需要斑点预处理步骤。

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