Cannabis detection from optical and RADAR data fusion: A comparative analysis of the SMILE machine learning algorithms in Google Earth Engine

Abstract

Accurate crop mapping for agricultural monitoring requires the use of robust image classification algorithms. Cultivation of illegal cannabis is common in the Bekaa plain, Lebanon, as in some other areas of the world (Morocco, Afghanistan). Identification of cultivation sites is money and time consuming since it highly relies on knowledge of cultivation areas and field surveys. Machine-learning-based image classification provides an alternative method for cannabis detection. This paper presents a comparative analysis of four machine-learning classifiers implemented in Google Earth Engine: Random Forest (RF), Gradient Boosting (GBT), Classification and Regression Tree (CART), and Support Vector Machine (SVM) for cannabis and other crop type classification. We implement and test several image fusion approaches for optical (Sentinel-2, Landsat) and Synthetic Aperture Radar (SAR) imagery (Sentinel-1) along with several combinations of surface textural features from Sentinel-1. Six different crop groups were classified over three years (2016, 2017, and 2018) in the Bekaa valley of Lebanon. In general, although SVM outperformed RF, GBT, and CART classifiers with an overall classification accuracy of more than 90%, RF and GBT provided more consistent results in terms of the cannabis cultivated area. SVM appears to be sensitive to the size of the training data. RF and GBT estimate an average cannabis area of 2800 ha in 2016, 2983 ha in 2017, and 5900 ha in 2018. Our results demonstrate that the fusion of radar and optical imagery can improve image classification accuracy by up to 5%. A marginal improvement in overall accuracy was observed when textural features were added. This is the first study that used image fusion and ML to estimate illegal cannabis cultivation areas in Lebanon and help evaluate the contribution of cannabis to the local economy. The combination of machine learning algorithms and imagery fusion proved reliable for crop classification in general and cannabis in particular.