# Multi-Classifier Pipeline for Olive Groves Detection
###### Abstract  
Pixel-based classification is a complex but well-known process widely used for satellite imagery classification. This paper presents a supervised multi-classifier pipeline that combined multiple Earth Observation (EO) data and different classification approaches to improve specific land cover type identification. The multi-classifier pipeline was tested and applied within the SCO-Live project that aims to use olive tree phenological evolution as a bio-indicator to monitor climate change. To detect and monitor olive trees, we classify satellite images to precisely locate the various olive groves. For that first step we designed a multi-classifier pipeline by the concatenation of a first classifier which uses a temporal Random-Forest model, providing an overall classification, and a second classifier which uses the result from the first classification. IOTA2 process was used in the first classifier, and we compared Multi-layer Perceptron (MLP) and One-class Support Vector Machine (OCSVM) for the second. The multi-classifier pipelines managed to reduce the false positive (FP) rate by approximately 40% using the combination RF/MLP while the RF/OCSVM combination lowered the FP rate by around 13%. Both approaches slightly raised the true positive rate reaching 83.5% and 87.1% for RF/MLP and RF/OCSVM, respectively. The overall results indicated that the combination of two classifiers pipeline improves the performance on detecting the olive groves compared to pipeline using only one classifier.  
pixel-based classification; Random-Forest; Multi-layer Perceptron; One-class Support Vector Machine +
Footnote †: journal:  
include Support Vector Data Description (SVDD) and One-class Support Vector Machine (OCSVM) [7]. In order to classify satellite imagery, a method based on several classification models which is called Multiple-Classifier System (MCS) is perceived to be able to improve the accuracy of classification performance [9]. According to [PERSON] [10], not only does MCS perform better than a single classifier, but it is also more robust especially for the overfitting issue. [PERSON] et al. [9] divided MCS into two main categories: concatenation system, and parallel system and some applications of the MCS concept are presented by [PERSON] [11] and [PERSON] [12]. The concatenation system uses the output of the first classifier as an input for the next, while for the parallel system, the results of several independent classifiers are combined based on different possible strategies to produce the final one [9].  
In this study, we tested and applied our classification pipeline within SCO-Live, a CNES funded project. SCO-Live is a collaborative project between ACRI-ST, ARGANS and CAPG (Community of agglomeration of the country of Grasse) [13], which aims to use olive trees as a bio-indicator to monitor climate change. If some previous study monitored olive trees health and water status, none present an accurate approach to solely identify olive graves and to correlate their phenological change with climate change. SCO-Live project is the first to propose a complete processing chain and form identification to ecological analysis in the Southeast of France. To follow and analyze olive trees through time, we first need to identify olive graves' location for our area of interest. A field campaign to localize all olive graves is too expensive and time consuming without the insurance to locate them all. Satellite imagery allow us to visualize a large area (100 km \(\times\) 100 km for Sentinel-2) and olive graves can be identify and extracted with a classification process. A temporal Random Forest algorithm is used within the IOTA2 processing chain (hereinafter referred to as the baseline pipeline).  
Our objective is to improve the performance of olive graves detection from the baseline pipeline using the supervised concatenated multi-classifier pipeline. Furthermore, we also compared the performances between a binary (2-class) and a unary (1-class) approach.