The surface morphology of electrospun fibers largely determines their application scenarios. Conventional scanning electron microscopy is usually used to observe the microstructure of polymer electrospun fibers, which is time consuming and will cause damage to the samples. In this paper, we use backscattering Mueller polarimetry to classify the microstructural features of materials by statistical learning methods. Before feeding the Mueller matrix (MM) data into the classifier, we use a two-stage feature extraction method to find out representative polarization parameters. First, we filter out the irrelevant MM elements according to their characteristic powers measured by mutual information. Then we use Correlation Explanation (CorEx) method to group interdependent elements and extract parameters that represent their relationships in each group. The extracted parameters are evaluated by the random forest classifier in a wrapper forward feature selection way and the results show the effectiveness in classification performance, which also shows the possibility to detect nonporous electrospun fibers automatically in real time.

中文翻译：

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基于互信息的Mueller矩阵数据特征提取以检测无孔电纺纤维

电纺纤维的表面形态在很大程度上决定了它们的应用场景。传统的扫描电子显微镜通常用于观察聚合物电纺纤维的微观结构，这很费时，并且会损坏样品。在本文中，我们使用反向散射Mueller旋光法通过统计学习方法对材料的微观结构特征进行分类。在将Mueller矩阵（MM）数据输入分类器之前，我们使用两阶段特征提取方法来找出代表性的偏振参数。首先，我们根据互信息测得的不相关MM元素的特征功率来过滤掉它们。然后，我们使用“相关说明”（CorEx）方法对相互依赖的元素进行分组，并提取代表每个组中它们之间关系的参数。