基于纳什均衡改进PWF的舰船检测方法

A modified PWF ship detection method based on Nash equilibrium

提出了一种利用极化白化滤波器(polarimetric whitening filter,PWF)作为舰船检测器的新方法。首先,介绍了基于Wishart分布的马尔可夫随机场(Markov random field,MRF)分类问题,利用相似性参数和距离函数构造平滑项,有效地利用极化信息。在利用纳什均衡对分类进行优化时,对参数的选取使用了Fisher准则,优化分类结果。对于最终的检测器,利用PWF构造表征差异的核函数,求解一个优化问题,得到了对应能量函数数据项检测函数,同时利用分类结果,使用模糊集构造平滑项检测函数,从而得到改进的极化检测器。最后,使用实际的极化合成孔径雷达(polarimetric synthetic aperture radar,Pol-SAR)数据对提出的方法有效性做验证并和已有方法进行比较,实验结果表明本方法检测率高,检测目标连续性好。

This paper proposes a new approach to ship detection based on polarimetric whitening filter （PWF）. The Markov random field （MRF） classification problem based on Wishart distribution is introduced, and similarity parameters and distance function are employed to construct the smooth item for using polarization information effectively. The Fisher criterion is used to select the parameters when using Nash equilibrium to optimize the classification result. For the final detector, the detection function of data items corresponding to the energy function is constructed by solving an optimization problem. For this problem, PWF structure is used to construct a kernel function of differences. At the same time, the classification result and fuzzy set are applied to construct the detection function of the smooth item. Then a new detector is proposed. Using polarimetric synthetic apertue radar （PolSAR） data, the effectiveness of the proposed algorithm is demonstrated and is compared with the existing algorithms, and the experiment verifies the high detection rate of the proposed method with the good continuity of target detection.