A Morphological Algorithm for the Detection of Linear Contrails

The climate impact of aviation can be separated into CO2 and non-CO2 effects, with the latter being potentially larger than the former. In thiscontext we are more specifically interested in condensation trails (hereafter contrails) and induced cirrus. Monitoring contrail formation and evolution isnecessary to understand their radiative effects and help the aviation industry to transition towards a more sustainable activity. Current research aimed atdetecting contrails is mostly based on geostationary satellite images because they allow to follow the contrail over a long period of time. However a majorshortcoming is that the formation phase of the contrails cannot be detected and larger, but older, contrails cannot always be attributed to the flightsthat produced them. To circumvent the problem that satellite images do not have a sufficient resolution to observe the contrail formation phase, weuse a ground-based hemispheric camera with a two-minute sampling rate as a complementary source of information. As a first step, we have developeda traditional morphological algorithm that will help preparing a sufficiently large labelled database as required to train a deep-learning algorithm. Ouralgorithm aims to detect whether each aircraft that passes in the field of view of the camera (as monitored from an ADSB radar) produces a contrail or not. We are thus able to relate contrail formation and evolution with aircrafttype, flight altitude and weather conditions. We start by focusing on the young linear contrails that appears just behind the aircraft. We also considerall weather conditions except completely cloudy conditions that prevents contrails to be observed. The algorithm combines various morphologicaltreatments to binarise the image and a linear Hough transform to identify straight lines in a direction close to the aircraft’s trajectory. Its performance is evaluated against a database that was manually annotated consisting of 400 images with 407 contrails. We find that our algorithm has a specificityof 97%, i.e. there are few false detections, but its sensitivity is about 55%, i.e. it is missing a significant fraction of contrail appearances. Looking inmore details, the sensitivity is 60% in clear-sky contidions but only 40% in conditions of a thin high cloud cover with superimposed contrails. Ananalysis of several years of contrail detection will be presented to determine precisely the fraction of contrail-producing flights and the associated weatherconditions with non-persistent and persistent contrails.