# Image Classification for Automated Image Cross-Correlation Applications in the Geosciences
## 1 Introduction  
Image cross-correlation (ICC) is a well-known methodology used in the geoscience field to measure earth surface dynamics and deformation phenomena [1; 2; 3; 4]. Ground-based ICC applications enable observations of relatively fast natural processes at a medium range and at a high spatiotemporal resolution. In addition, the low costs, minimal equipment required to conduct photographic surveys and the certain degree of automation used in data processing, make ground-based ICC applications valuable tools for use in monitoring phenomena, even in harsh environments [3]. The adoption of an automated procedure for processing monitoring data allows high-frequency-updated results to be obtained without the need for continuous human supervision, and can be applied in early warning system applications, thereby reducing human and economic costs [5]. Different automatic approaches used to collect and process monitoring data have been reported in literature, such as those for the inclinometer [6], total station [7; 8], ground-based SAR [9] and integrated systems [10].  
However, the major limitation of the technology used in the ICC approach for natural phenomena monitoring, is its dependence on visual conditions, since adequate illumination (i.e., sunlight) anda complete view of the scene (i.e., clear sky) are required. This prevents an automated ICC procedure being applied in near-real-time that could be used for warning purposes, as a human intervention to selecting suitable images is required. In addition, the presence of shadows formed by the interaction between direct illumination and surface roughness can cause errors in the cross-correlation (CC) process, and this problem has often been considered in literature [1; 11; 12; 13; 14; 15; 16]. The most popular solution used to avoid such effects is to consider pictures that have a similar visual appearance, and have been acquired in a similar light (usually in the middle of the day [12; 17; 18] when the length of shadows is reduced, due to the higher sun elevation angle). However, [PERSON] and [PERSON] [12], and [PERSON] et al. [15] proposed the use of images acquired in conditions of diffuse illumination (such as the evening hours) when any shadows are minimal or absent. Both of these approaches involve the manual selection of images, and images with a partial or absent view (caused by fog or the presence of obstacles), and with non-homogeneous illumination, are thus discarded. In this respect, [PERSON] et al. [14] developed a method for automatically discarding images taken during adverse meteorological conditions; this involved analysing the mean and standard deviation of colour values along predefined lines in the image. [PERSON] et al. [19] also automatically identified images containing artefacts, or where vision was obscured, using a posteriori statistical analysis based on image entropy. Furthermore, [PERSON] and [PERSON] [16] developed a method for automatically detecting shadowed areas, and then removing them from the CC computation.  
In our work, we describe a method of classifying images according to visible and illumination conditions that enables the selection of images with diffuse illumination. To correctly classify the images, we adopted a support vector machine (SVM) approach. This supervised machine learning method was originally developed by [PERSON] et al. [20] and [PERSON] [21]; it is a well-known methodology used in remote sensing and geoscience, and it has been adopted in a wide range of fields and applications (please, see reviews in [22; 23; 24; 25]). In addition, we present the use of completely automated processing in monitoring active gravitational processes through ICC. The principal innovation relies on applying autonomous image selection to ICC according to the a posteriori probability that an image belongs to a certain class of illumination.