# Multi-Classifier Pipeline for Olive Groves Detection
## 5 Conclusions  
Pixel-based classification is widely used for satellite imagery analysis. In this paper, we presented the use of multi-classifiers to compare its results and performances with the scenario using only one classifier. Within the frame of the SCO-Live project that aims to identify olive groves to monitor climate change impact, we evaluated our results based on the SCO-Live database and field data. The results indicated that multi-classifier pipelines (MLP and OCSVM) showed better performances in both detecting olive trees and lowering the misclassification compared to single classifier pipeline. In addition, we observed that using both one-class and two-class approaches as the second classifier in multi-classifier pipeline improves the quality of detection. However, from our field campaign, we found that a confusion between oak trees and olive trees often occurs during the classification process. A further and more complete spectral analysis of both species might highlight some spectral differences at specific season allowing a better discrimination of both types. Furthermore, with the newest version of the SCO-Live database, we can access more detailed information on the various farming conditions (irrigation, health) that affect olive trees' spectral response. This new information needs to be considered to further improve the resulting classification. Moreover, quantifying the computational cost and comparing other pairs of classifiers in the pipeline will be an interesting experiment to give more insight on the multi-classifier pipeline. Conceptualization, P.I.O. and A.-L.B. ; methodology, P.I.O. and A.-L.B. ; software, P.I.O. and L.K. ; validation, P.I.O. and A.-L.B. ; formal analysis, P.I.O. ; resources, A.M.; data curation, P.I.O. ; writing--original draft preparation, P.I.O. ; writing--review and editing, P.I.O. and A.-L.B. ; supervision, A.M.; project administration, A.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This research received no external funding. Not applicable. This research received no external funding. Not applicable. This research received no external funding. Not applicable. This research received no external funding. Not applicable. * [PERSON] (2018) [PERSON] Ensemble Learning Methods for Deep Learning Neural Networks, Machine Learning Mastery. 18 December 2018. Available online: [[https://machinelearningmastery.com/ensemble-methods-for-deep-learning-neural-networks/](https://machinelearningmastery.com/ensemble-methods-for-deep-learning-neural-networks/)]([https://machinelearningmastery.com/ensemble-methods-for-deep-learning-neural-networks/](https://machinelearningmastery.com/ensemble-methods-for-deep-learning-neural-networks/)) (accessed on 8 September 2022). * [PERSON] (2012) [PERSON] Bagging, Boosting and Ensemble Methods. In _Handbook of Computational Statistics: Concepts and Methods_; [PERSON], [PERSON], [PERSON]; Springer: Berlin/Heidelberg, Germany, 2012; pp. 985-1022. [CrossRef]
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