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Open AccessArticle

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

Research Institute for Hydrogeological Protection, National Council of Research of Italy, 10135 Turin, Italy
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Appl. Sci. 2019, 9(11), 2357; https://doi.org/10.3390/app9112357
Received: 1 April 2019 / Revised: 9 May 2019 / Accepted: 4 June 2019 / Published: 8 June 2019
(This article belongs to the Special Issue Advances in Digital Image Correlation (DIC))
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. View Full-Text
Keywords: image cross-correlation; monitoring; geosciences; automated systems; machine learning; image classification; image shadowing image cross-correlation; monitoring; geosciences; automated systems; machine learning; image classification; image shadowing
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MDPI and ACS Style

Dematteis, N.; Giordan, D.; Allasia, P. Image Classification for Automated Image Cross-Correlation Applications in the Geosciences. Appl. Sci. 2019, 9, 2357.

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