  Ensembling is a method that aims to maximize the detection performance by
fusing individual detectors. While rarely mentioned in deep-learning articles
applied to remote sensing, ensembling methods have been widely used to achieve
high scores in recent data science com-petitions, such as Kaggle. The few
remote sensing articles mentioning ensembling mainly focus on mid resolution
images and earth observation applications such as land use classification, but
never on Very High Resolution (VHR) images for defense-related applications or
object detection.This study aims at reviewing the most relevant ensembling
techniques to be used for object detection on very high resolution imagery and
shows an example of the value of such techniques on a relevant operational
use-case (vehicle detection in desert areas).
