Abstract
Abstract. Circle and ellipse detection have been a widely discussed topic among the computer vision community. Applying circle detection methods to satellite images can bring valuable information on urban or industrial areas. A limitation of such methods lies in the resolution of the object to detect. The smaller they are in the image, the harder it becomes to detect them accurately. In this paper, we explore several circle detection methods adapted to low resolution satellite images. An algorithm based on level-lines detection and classification will be presented in this paper along with other well known algorithms. The methods are evaluated in the context of oil tank detection in Sentinel-2 images where those circular objects can have an observed diameter as small as 2 pixels.
Highlights
Recognizing circular objects in images can bring insightful information on a scene
In this paper we propose to compare these methods to another one based on Closed Level-Line Extraction inspired by the Fast Level-Line Transform (Monasse, Guichard, 2000a) on which the isoperimetric ratio is computed to detect circles
In the work presented here we will focus on the use of circle detectors to recognize oil tanks in optical satellite images
Summary
Recognizing circular objects in images can bring insightful information on a scene. This is all the more interesting on satellite images with a wide coverage of the Earth’s surface. Several papers have proposed methods to tackle this problem such as the one described in (Han, Xu, 2012, Cai et al, 2014, Ok, Baseski, 2015, Zhang et al, 2015, Soundrapandiyan, 2017) and more recently in (Zalpour et al, 2019) and (Tadros et al, 2020) These papers use two properties of oil tanks. Han et al (Han, Xu, 2012) use geometrical properties of circles to detect them from a saliency map They filter their detections and extract oil depots by using a graph-based clustering method. In the work presented here we will focus on the use of circle detectors to recognize oil tanks in optical satellite images
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