# Multi-Classifier Pipeline for Olive Groves Detection
## 4 Discussion  
The results have demonstrated that the significant difference between the baseline pipeline and the multi-classifier pipeline was highlighted in the change of pixels from Olive class to Non-olive class. As we simultaneously observed a decrease of FPR with both MLP and OCSVM, meaning that we reduced the number of wrongly classified pixels in Olive class, we can assume that most of pixels firstly classified as olive were misclassified. For confirmation, we conducted a field survey for some areas where the classification maps showed different predictions between baseline and multi-classifier pipeline. Most of our field observations validated that the pixels firstly classified as olive by the baseline pipeline but not by multi-classifier pipeline were indeed not olive trees in the field. In some areas, we could find green oak and white oak trees in the misclassified regions of the baseline pipeline. We hypothesize that these oak trees may possibly contribute to the confusion of the classification by the model in the baseline pipeline.  
In addition to the decrease in misclassification of olive trees, we could also observe from the TPR calculation of the SCO-Live project field observation data that the correct detection of olive trees rose slightly despite the reduction of Olive class coverage in the multi-classifier pipeline. This suggests that the multi-classifier pipeline improves the overall performance of olive detection, which aligned with the theory of using more than one classifier in a system to target a better accuracy at the price of complexity [21]. Furthermore, the improvement of detection can also be linked to the division of classification tasks in the multi-classifier pipeline. By assigning the first classifier to predict all vegetations first and letting the second classifier to further classify the vegetations into olive trees or not, the pipeline performs hierarchical classification [22], where a specified land type is identified after a more a general classification.  
With SVM as a one-class approach and MLP as a two-class approach, the results proved that both have raised the performance of olive detection. In our use-case, because we need to minimize the noise around of the classified olive trees pixels, we perceived that the two-class MLP is more suitable for our project as it lowered the FPR significantly while maintaining a good TPR. However, we found that using one-class SVM in our data can achieve even better results in detecting the olive trees (positive data). Therefore, the use of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pipeline** & **True Positive Rate** \\ \hline IOTA2 & 81.7\% \\ IOTA2 + MLP & 83.5\% \\ IOTA2 + OCSVM & 87.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: True positive rate of the three pipelines.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pipeline** & **False Positive Rate** \\ \hline IOTA2 & 42.5\% \\ IOTA2 + MLP & 1.4\% \\ IOTA2 + OCSVM & 28.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: False positive rate of the baseline pipeline and the multi-classifier pipelines.  
one-class approach can be an interesting and promising solution for other use cases where we only have data for one category of land cover.