Olive tree and shadow instance segmentation based on Detectron2

TLDR: In this article , the use of the Deep Learning approach Detectron2 in order to produce instance image segmentation, to make distinguishing between the olive tree, its shadow and the soil.

Abstract: Agriculture in Tunisia is a very important economic sector especially olive tree cultivation. Monitoring the latter from space using remote sensing sensors remains challenging and requires to develop adapted code. Detection olive tree size can help to monitor the growth. To do it, we propose in this work to detect tree crown on high resolution satellite images, in particular Pléiades. To achieve this goal, we proposed the use of the Deep Learning approach Detectron2 in order to produce instance image segmentation, to make distinguishing between the olive tree, its shadow and the soil. Detectron2 was trained using a database of realistic images generated by the DART model. The evaluation results prove the validity of our proposed approach also the visual inspection shows good agreement with the reality.