双色中波红外图像差异统计特征的选择与提取

 针对双色中波红外图像特征数目庞大，不易于对其双波段成像差异进行综合分析的问题，提出一种基于统计特征的三级特征选择模型。该模型先对双波段的原始统计特征进行标准化处理，然后根据其在四组双波段图像中的分布，经过显著差异级、一致差异级、特殊差异级的分析，选择出能够满足双波段图像差异分布规律的有效统计特征。在特征选择的基础上，利用局部线性嵌入（LLE）的特征提取方法对有效统计特征进行降维处理。实验数据表明，经该特征选择与提取后的单维特征能够综合反映双波段图像的差异幅度，达到了利用少量特征对双波段差异综合分析的目的。

Selection and extraction of difference statistical features based on dual color MWIR images

Feature space dimension of the dual-color mid-wave infrared (MWIR) image is large and difficult to conduct a comprehensive analysis of its dual-band imaging differences. The three-level feature selection model based on the statistical features was proposed. This model firstly standardized the dual-band's original statistical features, then with the distribution of four-group dual-band images, through the analyses of the significant-difference level, consistent-difference level and special-difference level, chose the effective statistical features which satisfied the dual-band image differences distribution regularity. Based on the feature selection, this paper used locally linear embedding (LLE) to reduce the dimension of the effective statistical features. The experimental data indicate that the one-dimensional features after feature selecting and extracting can show the differences in rate of dual-band image, achieving the purpose of using small amount features to comprehensively analyze the differences in dual-band.