Data analysis methods have been extensively used in active thermography for defect identification. Among them, principal component thermography (PCT) is popular for dimensionality reduction and feature extraction. PCT summarizes the thermal images with a small number of empirical orthogonal functions that better reflect the information of defects. However, PCT does not induce sparsity, which limits the interpretation of PCT results. Recently, sparse PCT (SPCT) has been proposed to provide more interpretable analysis results. However, SPCT does not consider the spatial connectivity between pixels, omitting the fact that a defective region is usually spatially connected. In this article, a novel thermographic data analysis method is proposed to overcome the shortcomings of the existing methods. The proposed method imposes both spatial connectivity and sparsity constraints in PCT. Finally, one case study on an ancient marquetry sample and another on a carbon fiber-reinforced polymer composite illustrate the feasibility of the proposed method.
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