差异特征频次属性的分布构造对建立双模态红外图像差异特征多属性融合有效度分布合成具有重要意义。针对双模态红外图像差异特征频次属性分布构造的问题，提出了基于 K最近邻（KNN）概率密度估计的差异特征频次分布构造方法。利用累积分布函数得到差异特征频次真实序列值，计算所构造的差异特征频次分布中具有统计意义的频次序列值与真实序列值的相似性测度，对结果进行了验证。实验结果表明，将非参数概率密度估计运用于差异特征频次分布构造中具有可行性，且本文方法相较于 MISE最优带宽高斯核密度估计更能准确构造差异特征频次分布。

The distribution of difference feature frequency is crucial for establishing a multi-attribute fusion validity distribution synthesis of difference features of bimodal infrared images. To construct different feature frequency distributions of bimodal infrared images, a method of constructing a difference feature frequency distribution based on the K nearest neighbor(KNN) probability density estimation is proposed. The cumulative distribution function is used to obtain the true sequence value of the difference feature frequency; subsequently, the similarity measure of the statistically significant frequency sequence value and the real sequence value in the constructed frequency distribution of the difference feature are calculated. Experimental results show that non-parametric probability density estimation can be applied to the frequency distribution of difference features. The proposed method can accurately construct the frequency distribution of difference features compared with the MISE optimal bandwidth Gaussian kernel density estimation.