# Image Classification for Automated Image Cross-Correlation Applications in the Geosciences
## 3 Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Analysis** & **Site** & **Environment** & **Image Type** & **\# Images** & **Resolution [px]** & **Dataset Symbol** \\ \hline \multirow{2}{*}{Shadow effect} & Monesi & Alpine village & Shaded relief & 10 & \(656\times 875\) & MNS1 \\  & Monesi & Rotational landslide & RGB & 30 & \(3456\times 5184\) & MNS2\_a \\ \hline \multirow{6}{*}{Image classification} & Acceglio & Bedrock outdoor & RGB & 278 & \(3456\times 5184\) & ACC \\  & La Saxe & Rockslide & RGB & 159 & \(3240\times 4320\) & SAXE1 \\  & La Saxe & Rockslide & RGB & 163 & \(1536\times 2048\) & SAXE2 \\ \cline{1-1}  & La Saxe & Rockslide & RGB & 268 & \(3312\times 4416\) & SAXE3\_a \\ \cline{1-1}  & Monesi & Rotational landslide & RGB & 117 & \(3456\times 5184\) & MNS2\_b \\ \cline{1-1}  & Planpincieux & Glacier & RGB & 225 & \(3456\times 5184\) & PPCX1 \\ \cline{1-1}  & Planpincieux & Glacier & RGB & 195 & \(3456\times 5184\) & PPCX2\_a \\ \hline Automated ICC procedure & La Saxe & Rockslide & RGB & 139 & \(3312\times 4416\) & SAXE3\_b \\ \cline{1-1}  & Planpincieux & Glacier & RGB & 1549 & \(3456\times 5184\) & PPCX2\_b \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Datasets adopted in the current study. The first column lists the type of analysis employed using the various datasets. Also presented is the site where images were acquired for each dataset, the type of environment and image, the number of elements, the image resolution and the symbol attributed to the dataset. An ensemble of seven datasets representing scenes from various natural environments was used to study the image classification methodology and to ensure that the method could be generalised. Specifically, datasets ACC, SAXE1, SAXE2, SAXE3_a, MNS2_b, PPCX1 and PPCX2_a were employed, and samples of images from these datasets acquired in different visual conditions are shown in  
Figure 4c1-i3. Each dataset comprises photos belonging to different classes (i.e., acquired in different illumination and visual conditions), as catalogued during the process described in Section 2.3. The numbers of images in each class employed from the different datasets are presented in Table 2.  
The images included in all the datasets were acquired during different years and seasons. The results showed that the SVMs classified the images accurately, even when dissimilar environmental conditions and morphological changes were presented. However, although it is considered that SVM training by adopting photos taken during specific seasons could improve its performance, this could result in a loss of generality. The last application is automated ICC processing, and this was conducted using two-hourly time-lapses. The first dataset (PPCX2_b) comprised more than 1500 images, and it was collected to monitor the activity of the Planpincieux glacier from 18 May to 3 October. During this period, the automated ICC procedure described in Section 2.4 was applied in real time to monitor the glacier's evolution. The second dataset used was SAXE3_b, where 139 images of the Mont de La Saxe landslides were selected. Table 3 shows the number of images employed from PCCX2_b and SAXE3_b datasets and their distribution among the different classes (considering also the bivalent classes described in Section 2.4). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **SunLight** & **DiffLight** & **NoVis** \\ \hline A & 94 & 97 & 87 \\ B & 77 & 45 & 37 \\ C & 53 & 60 & 50 \\ D & 95 & 96 & 77 \\ E & 59 & 31 & 27 \\ F & 75 & 75 & 75 \\ G & 65 & 65 & 65 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Numbers of images belonging to different classes used in each dataset. Figure 3: Locations of sites corresponding to the various datasets adopted. The black circle, square, and triangle correspond to Monesi di Mendatica, the Acceglio rock face, and the Mont de La Saxe landslide and Planpincieux glacier, respectively. This image was produced using the Matlab package borders [47]. Figure 4: Examples of images taken from different datasets.