Comparison of principal component analysis and multi-dimensional ensemble empirical mode decomposition for impact damage segmentation in square pulse shearography phase images

Composite materials have mechanical behavior comparable to metallic alloys, with the benefit of being lighter. However, due to their anisotropy, integrity characterization of impact damages remains a challenge. Non-Destructive Testing (NDT) methods are useful in this context, as they achieve success in evaluation while they avoid modifications in the characteristics of the piece. Shearography is an NDT that reveal changes on a surface in response to a load. Yet, shearography outputs carry several unwanted characteristics along with the defect, like background patterns, light changes, and noise. Image segmentation techniques can enhance the capability of automatic measurement of defective areas and also aid supervised methods and feature extractors, which rely on images as inputs. Yet, most of the times image pre-processing is required for better and useful results in segmentation. Principal Component Analysis (PCA) and Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) allow this to be done at the same time that they deal with background and light changes, as they decompose the image. Using Matthews Coefficient as a metric for masks comparison, it is shown that MEEMD has better results than PCA, and with lower expanded uncertainty.