IRNDT Inspection Via Sparse Principal Component Thermography

The recent applications in the field of thermography and Infrared Non-Destructive Testing (IRNDT) involved many different research fields, and in most of these applications well-known infrared approaches have been utilized for thermal image enhancement, thermal image segmentation, and particularly defect segmentation in IRNDT. Principal Component Analysis (PCA) or Principal Component Thermography (PCT) is one of these methods that has been countlessly used and it is unequivocally one of the constantly referred approaches in this field. Unfortunately, it suffers from being a linear transformation and besides that, finding appropriate basis through its eigenimage decomposition is the shortcoming. Here, an application of non-linear eigen decomposition using Sparse Principal Component Analysis/Thermography (Sparse-PCA or Sparse-PCT) is addressed for segmentation of defects inherent to two hybrid composites (carbon and flax fiber epoxy prepregs). The results indicate considerable segmentation performance when it is compared to similar approaches.