A Contrario Oil Tank Detection with Patch Match Completion

Abstract

The energy sector is a key industry in the global economy and monitoring oil storage provides valuable insights into the economic state of a country. Our aim is to detect oil tank farms as accurately as possible using Sentinel-2 images. An a contrario clustering method is used to group by density the result of a circle detection step. Then, a patch-match procedure is used to complete the tank detection. Although most existing methods are designed to work on high-resolution images, the proposed method is designed for low-resolution images; we also propose an adaptation to high-resolution images.