收稿：2018-07-12，
录用：2018-8-24，
纸质出版：2019-05
移动端阅览
在已有的极化合成孔径雷达(PolSAR)图像恒虚警(CFAR)检测方法中，存在着高分辨下杂波模型适用性差的难题。为解决此问题，提出了一种
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分布下虚警概率具有闭合解析表达形式的CFAR检测方法，并定义虚警损失率(CFAR Loss
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)参数用以量化评估CFAR检测方法的恒虚警保持效果。首先，在乘积模型框架下，引入了逆Gamma纹理变量假设，推导出了多视极化白化滤波(MPWF)检测量的概率密度函数(PDF)。然后，对MPWF检测量的概率密度函数积分得到了虚警概率关于CFAR检测阈值的解析表达式，并设计了相应的CFAR检测流程。最后，采用仿真数据和AIRSAR实测数据对已有方法和新方法进行了算法运行时间、检测量拟合性能及目标检测性能对比。实验结果表明，方法运行时间比已有方法缩短3至30倍，具有良好的实时性；日本玉野地区的AIRSAR实测数据结果表明
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分布对高分辨不均匀海区具有良好的拟合性能，且新方法在
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分布和非
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分布海区均能有效检测出目标，鲁棒性较强，相比其他检测方法品质因数(FoM)平均高出15.78%；
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分析结果表明新方法具有良好的恒虚警保持性能，同时指出杂波对数累积量散点距离
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分布曲线越近，新方法的恒虚警保持效果越好。
Poor applicability has been a common issue among high-resolution clutter models that use the existing constant false alarm rate (CFAR) detection methods for polarimetric synthetic aperture radar (PolSAR) imageries. To solve the problem
a CFAR detection method with
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distribution and closed analytical expression for PolSAR imageries is proposed. CFAR loss (
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) is used to quantitatively evaluate the maintenance performance of CFAR detection methods. First
the probability density function (PDF) of the multi-look polarization whitening filter (MPWF) metric is derived on the basis of product modeling by combining the hypotheses of inverse Gamma distribution texture. Second
the PDF of the MPWF metric is integrated
and the analytical expression of the false alarm rate with respect to the CFAR detection threshold is obtained. The process of the proposed CFAR detection method is also designed. Finally
the simulation data and the AIRSAR real data are used to compare the running times of the methods
the fitting performances of the detection metrics
and the target detection performances of the existing and proposed methods. Results show that the running time of the proposed method is 3—30 times shorter than those of the existing methods
and the real-time performance of the proposed method is also enhanced. The analytical results of the AIRSAR real data of Tamano (Japan) show that
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distribution has a good fitting performance with high-resolution non-uniform sea areas. The proposed method can detect targets effectively in both
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and non-
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distribution sea areas
and its robustness is better than those of the existing methods. Moreover
the figure of merit of the proposed method is 15.78% higher than those of the other detection methods. The analytical results of
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indicate that the proposed method has good CFAR maintenance performance. Moreover
the closer the clutter log-cumulant scatter points are to the
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distribution curve
the better the CFAR maintenance performance of the proposed method.
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