The subspace learning (dimensionality reduction) algorithms have played an important role in the analysis of thermographic data: a key step in infrared thermography-based nondestructive testing of subsurface defects in composite materials. However, one of its branches, manifold learning, with excellent ability to preserve local data structure, is rarely applied. In this article, a spatial-neighborhood manifold learning (SNML) framework is proposed for thermographic data analysis. Different from traditional manifold learning methods, SNML uses the spatial-neighborhood information instead of the traditional k-nearest neighbors, or ε-neighborhood, to construct the adjacency graph. This overcomes the difficulty of parameter selection and extracts local features in images in a more reasonable way. Additionally, the data preprocessing step and the means of thermographic data normalization in the proposed framework are discussed. For performance comparison, three traditional manifold learning methods are also implemented. The experiments on carbon fiber-reinforced polymer specimens demonstrate the validity and feasibility of SNML.

中文翻译：

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用于聚合物复合材料缺陷无损检测的空间邻域流形学习


子空间学习（降维）算法在热成像数据分析中发挥了重要作用：这是基于红外热成像的复合材料次表面缺陷无损检测的关键步骤。然而，其分支之一流形学习具有出色的保存局部数据结构的能力，但很少得到应用。在本文中，提出了一种用于热成像数据分析的空间邻域流形学习（SNML）框架。与传统的流形学习方法不同，SNML使用空间邻域信息而不是传统的k近邻或ε邻域来构建邻接图。这克服了参数选择的困难，以更合理的方式提取图像中的局部特征。此外，还讨论了所提出的框架中的数据预处理步骤和热成像数据标准化的方法。为了进行性能比较，还实现了三种传统的流形学习方法。碳纤维增强聚合物试件的实验证明了SNML的有效性和可行性。