Image Classification for Automated Image Cross-Correlation Applications in the Geosciences

Featured Application This study proposes a method that enables the automatic application of image cross-correlation when monitoring any displacements in the geosciences. As such, it solves one of the main current image processing issues: The requirement of manual image selection. The method reduces the need for extensive financial and human resources when conducting surveys, and it can be applied in a preventive warning context. Abstract In Earth Science, image cross-correlation (ICC) can be used to identify the evolution of active processes. However, this technology can be ineffective, because it is sometimes difficult to visualize certain phenomena, and surface roughness can cause shadows. In such instances, manual image selection is required to select images that are suitably illuminated, and in which visibility is adequate. This impedes the development of an autonomous system applied to ICC in monitoring applications. In this paper, the uncertainty introduced by the presence of shadows is quantitatively analysed, and a method suitable for ICC applications is proposed: The method automatically selects images, and is based on a supervised classification of images using the support vector machine. According to visual and illumination conditions, the images are divided into three classes: (i) No visibility, (ii) direct illumination and (iii) diffuse illumination. Images belonging to the diffuse illumination class are used in cross-correlation processing. Finally, an operative procedure is presented for applying the automated ICC processing chain in geoscience monitoring applications.