# Image Classification for Automated Image Cross-Correlation Applications in the Geosciences
## 4 Results
### Automated ICC Procedure  
To evaluate the results of the automated ICC procedure, image classification and selection performances were analysed. Table 5 shows the results of automatic classification. In the case of images belonging to the bivalent class, an image was considered to be correctly classified if it was classified into one of the two classes that had been identified manually. Results therefore show that the classification error for dataset SAXE3_b is 4.3%, while the selection error is 0%. For dataset PPCX2_b, the misclassification is 7.7%, while the error in determining whether the correct image should be included in the ICC processing is 13%. For both datasets, the classification error is comparable with the value obtained during the cross-validation analysis. However, the selection error is related more to the occurrence of DiffLight false positives, which are higher in dataset PPCX2_b but considerably lower in SAXE3_b. We also compared the ICC results obtained with automatic images selection through SVM, and the results of the automatic image selection using images selected at fixed hours, in conditions of direct and diffuse illumination. The correlation and mean absolute error (MAE) were computed with respect to results obtained using the manual selection of images (Table 6). The comparison was conducted pixel-by-pixel; therefore, more than 2\(\cdot\)10\({}^{5}\) and 5\(\cdot\)10\({}^{5}\) elements were considered for datasets SAXE3_b and PPCX2_b, respectively. The results obtained with SVM selection show much higher correspondence with ICC results than the other two methods. Two principal factors provided lower performances in the other two selection methods: (1) As the state of view of the images was not controlled, it was possible to include images with obscured or partial views in the number of images that were processed; (2) the images with direct illumination suffered from the negative effects of shadows; therefore, any results for images with direct illumination are of a lower quality. Furthermore, the correlation along the vertical direction was higher than that along the horizontal direction. As previously observed in the synthetic image analysis, the incidence of the sun's azimuth angle is greater because its value varies more than the sun's elevation, which influences the vertical direction. The MAEs of the vertical direction were thus greater because the values of displacement of these dimensions were greater. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & \(\rho_{x}\) & **MAE\({}_{x}\)[px]** & \(\rho_{y}\) & **MAE\({}_{y}\)[px]** \\ \hline \multirow{3}{*}{SAXE3\_b} & SVM & 0.69 & 0.18 & 0.84 & 0.24 \\  & SunLight & 0.12 & 1.30 & 0.19 & 1.44 \\  & DiffLight & 0.38 & 0.44 & 0.69 & 0.57 \\ \hline \multirow{3}{*}{PPCX2\_b} & SVM & 0.31 & 0.34 & 0.71 & 0.51 \\  & SunLight & 0.02 & 1.78 & 0.14 & 2.21 \\ \cline{1-1}  & DiffLight & 0.08 & 1.71 & 0.22 & 1.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Correlation \(\rho\) and MAE of two dimensions for dataset D1. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**SunLight**} & \multicolumn{2}{c}{**NoVis**} & \multicolumn{2}{c}{**DiffLight**} & \multicolumn{2}{c}{**Classification Error**} & \multicolumn{1}{c}{**Selection Error**} \\ \hline SAXE3\_b & 22 & 2 & 4 & 11 & 2 & 0 & 74 & 22 & 2 & 4.3\% & 0\% \\  & 86\% & & 100\% & & 98\% & & & & \\ \hline PPCX2\_b & 781 & 45 & 52 & 166 & 23 & 26 & 335 & 79 & 42 & 7.7\% & 13\% \\  & 94\% & & 88\% & & 91\% & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of automatic classification and selection for datasets SAXE3_b and PPCX2_b. The number of images for each class are reported and correspond to exact, bivalent and erroneous classification. The percentage of correctly classified images is shown in the row below (in relation to the sum of exact and bivalent classifications). The two columns on the right show the percentage overall classification error and selection error, respectively.