Statistical analysis of IR thermographic sequences by PCA

Automatic processing of IR sequences is a desirable target in Thermal Non Destructive Evaluation (TNDE) of materials. Unfortunately this task is made difficult by the presence of many undesired signals that corrupt the useful information detected by the IR camera. In this paper the Principal Component Analysis (PCA) is used to process IR image sequences to extract features and reduce redundancy by projecting the original data onto a system of orthogonal components. As a thermographic sequence contains information both in space and time, the way of applying PCA to these data cannot be straightforwardly borrowed from typical applications of PCA where the information is mainly spatial (e.g. Remote Sensing, Face Recognition). This peculiarity has been analysed and the results are reported. Finally, in addition to the use of PCA as an unsupervised method, its use in a 'learning and measuring' configuration is considered.