# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 2 Materials and Methods
### Classifier Algorithms
#### 2.4.3 Evaluation  
The aim of this section is to compare two approaches, using two different classifiers (RF and SVM) with their parameterization.  
The classifier test should be valid, and should demonstrate the classifier's performance under conditions as close as possible to the production environment. The results obtained are averaged over three random, uniformly distributed trials, from the training and validation samples. This approach ensures that the results are not influenced by any specific split between the training and validation samples. Optimization of the two classifiers is performed using three-fold cross-validation. The basic idea of this validation is to divide the training dataset into 3 subsets of equal size, with the ratios 2/3 and 1/3 being used for the training and validation sets, respectively. For each two subsets, the classifier is trained, and the remaining subset is used for validation.  
As shown in Figure 3, the database is split into separate 3 sets. The classifier is trained and validated 3 times. Finally, the results are collected from each cross-validation run, for statistical analysis, and the predictions are compared. The training and the validation steps are then applied on the whole database. As with any supervised classification, validation is an essential step when evaluating the quality of the classification results, and is performed by comparing the classified data with the reference data (ground-truth measurements). This comparison is often expressed by means of a confusion matrix, which facilitates the understanding of the results, and provides detailed information concerning the effects of incorrect predictions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Configurations** \\ \hline Number of trees (k) & 100 \\ Maximum depth of the tree & 5 \\ Minimum number of samples in each node & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameterization of random forest classifier.  
Figure 3: Three-fold cross-validation.  
The confusion matrix is represented by a square array, whose columns correspond to the predicted values, according to the classification, and whose rows represent the reference data. The correctly classified pixels' numbers in each class are represented in the matrix diagonal, whereas the elements lying outside the diagonal correspond to confusions. Two main indices are then computed from the matrix, to evaluate its classification:  
* The overall accuracy, which is the ratio of the well-ranked pixels to the total number of validation pixels. This index varies between 0 (no pixel correctly classified) and 1 (100% of pixels are well classified)
* The Kappa coefficient (Kc) is a measure of the accuracy, which takes into account positive results that occur at random. The quality of the classification is assessed according to the value of this coefficient, which can vary over the range between -1 (very poor) and 1 (excellent).  
To evaluate the influence of the class label, using RF and SVM, the OA and Kc are computed from each confusion matrix, over a set of different simulations.