## 2 Basic Principles of Digital Image Correlation (DIC)  
According to the author of [44], several digital approaches can be used to analyze and manipulate available imagery datasets, and different types of information can be extracted depending on the typology of the chosen image processing technique. Basically, digital image processing techniques are founded on the extraction of ground change information via comparisons between different types of images (e.g., satellite-based, airborne or ground-based imagery) collected at different times over the same area and scene. DIC is an optical-numerical measurement technique that can provide full-field 2D surface displacements or deformations of any type of object. Deformations are calculated via comparisons and processing of coregistered digital images of the surface of the same \"object\" collected before and after the deformation event [45]. In most cases, the DIC technique allows for displacements/deformations to be measured without the installation of sensors/reflectors in the measured object; i.e., it can be considered to be a fully remote measurement system [46]. However, the basic requirement for DIC analyses is the occurrence of a random speckle pattern on the object's surface, which is essential for obtaining a unique solution in the correlation process. Essentially, identifying the correspondence between single pixels in two images is impossible because the intensity value of a single pixel can typically be found in thousands of other pixels in the post-event image. Consequently, unique correspondence does not occur [47]. Therefore, consistency between two speckle patterns is accomplished by considering a pixel and its neighborhood in the pre-event image (\(f\)) and searching for the same subset in the post-event image (g) (Figure 1). The size of the subset depends on the granularity of a non-repetitive, isotropic, high-contrast pattern (denoted as the speckle pattern) [48]. Hence, the correlation processes have to refer to a region of interest (ROI). The ROI represents an area of the picture chosen by the operator, and it is overlaid only on the object to be correlated. Therefore, between the reference and the deformed images, the ROI represents the analysis mask in which the correlation algorithm operates. Caution must be exercised when selecting the subset size. According to the authors of [49; 50; 51], the chosen optimum subset size must not be too small and is governed by the granularity of the speckle pattern. A subset that is too large, however, cannot be used to describe large heterogeneous deformations and will substantially worsen the sub-pixel accuracy. Therefore, to guarantee a reliable displacement measurement, the subset has to be chosen depending on the typology of the performed images, because the typology can lead to high sub-pixel accuracy measurements for images with larger contrasts. Furthermore, the step size, which is the shift of the subset during the correlation process, has to be set carefully. Generally, the subset size should at least have the dimensions of the largest blob appearing in the speckle pattern [52]. This procedure is repeated for a grid of pixels spaced at regular intervals (step size). The information density is increased when the step size is small. Several functions are used to match the subset from the pre-event image to the post-event image.