## 5 Results and Discussion
### Classification Accuracy with Regard to Data Sources and Features
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classified Data**} & \multicolumn{6}{c}{**Reference Data**} \\ \cline{2-7}  & **Aubergine** & **Cucumber** & **Pepper** & **Tomato** & \(\sum\) & **UA (\%)** \\ \hline
**Aubergine** & 36 & 7 & 5 & 32 & 80 & 45.0 \\
**Cucumber** & 10 & 42 & 1 & 9 & 62 & 67.7 \\
**Pepper** & 3 & 12 & 157 & 20 & 192 & 81.8 \\
**Tomato** & 15 & 10 & 6 & 329 & 360 & 91.4 \\ \(\sum\) & 64 & 71 & 169 & 390 & 694 & \\
**PA (\%)** & 56.3 & 59.2 & 92.9 & 84.4 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix for Landsat 8 time series dataset and no GLCM strategy. PA, producer’s accuracy. In Figure 5 is depicted the evolution of the time series profile. The cucumber greenhouse located in the center, planted on 27 August 2013, appears without white paint on 11 August (Figure 5b,f). It seems to be whitewashed in Figures 5a,c,e,g (30 and 12 September, respectively), and finally, this greenhouse covering is shown without any trace of paint in November (Figure 5d,h). Moreover, November (Figure 5h) turns out to be the month when most of the greenhouses allow one to visually perceive (false color images) the presence of crops through an increase in the NIR reflectance. This is not the case of the greenhouse located just on the right of the aforementioned cucumber one. In fact, it is a tomato crop that was whitewashed in September, and it had not yet been washed in November (Figure 5h). The capacity of the DT approach was used to select the object-based features and their cutting thresholds that best fit to every type of under-greenhouse crop. In view of the complexity of the computed DT, presenting up to 34 terminal nodes for the case shown in Table 5 (Landsat 8 time series dataset and no GLCM strategy), just the first divisions of that DT (only nine terminal nodes) are presented (Figure 6). At the bottom and outside of each black box in Figure 6 appears the object-based feature that is causing the next split in the decision tree. Below, the readers can see the figure that represents the cutting threshold for the split. Oval nodes are terminal nodes belonging to a particular class. Thereby, the OA value attained with the pruned DT shown in Figure 6 was 75.9%, considering the last box (\(N=419\)) as the Class 4 (Tomato) terminal node. It is worth noting that the object-based feature located at the top of the tree was the mean value of SWIR2 for the Landsat 8 pan-sharpened orthoimage taken on 11 August (AGO11 Mean SWIR2). SWIR2 reflectance values higher than 44.85% (right branch of the tree) were mainly associated with greenhouses that received an important dose of white paint in August, so pointing to pepper crops. In fact, 149 out of the 191 selected greenhouses with higher SWIR2 turned out to be pepper crops. The remaining high SWIR2 objects in August were identified as tomato (33 greenhouses belonging to Class 4), aubergine (eight greenhouses for Class 1) and cucumber (one greenhouse belonging to Class 2). The next steps in that branch of the DT were addressed to separate these tomato greenhouses. In this way, features, such as the mean value for the green band reflectance on 24 June, the Green Vegetation Index (GVI) on 26 July and GVI in November, were chosen to carry out this task. At the end of June, pepper greenhouses were being prepared to receive the plants, so they were usually bare and presented a lower value for the green band reflectance. Regarding GVI values for the pepper crops, they were often higher at the end of July and lower in November as compared to other crops in the study area. On the left branch of the DT (Figure 6) we observe most of the aubergine (72), cucumber (61) and tomato (327) crops, which received low doses of white paint (SWIR2 reflectance \(\leq 44.85\%\)), but it also contained 43 greenhouses of Class 3 (Pepper). The SWIR1 mean value on 26 July was used to extract these pepper crops with a high dose of white paint when transplanted early.