Clustering Sargassum Mats from Earth Observation Data

Estèle Glize (1) , Marie-José Huguet (1) , Marc Lucas (2) , Marion Sutton (2) , Gilles Trédan (3)

1 LAAS-ROC - Équipe Recherche Opérationnelle, Optimisation Combinatoire et Contraintes
2 CLS - Collecte Localisation Satellites [Toulouse]
3 LAAS-TSF - Équipe Tolérance aux fautes et Sûreté de Fonctionnement informatique

Sargassum seaweed forms large floating mats drifting on the oceans. These mats are increasingly beaching on Caribbean islands, threatening the local wildlife and economies. This paper focuses on their tracking from space, in order to monitor these mats. More specifically, we focus on clustering sargassum mats on satellite images. This constitutes an important building block of the sargassum monitoring system, which then predicts the drift of those clusters to anticipate beachings and warn the local authorities. The difficulty of the clustering operation comes from the noisy nature of input data: image artefacts, partial cloud occlusion, mats discontinuities due to sea conditions, and the difficulty of acquiring ground truth data. This paper details our approach to overcome those challenges. We propose a method (hereafter named Sargassum Mats Detection Method-SMDM) that improves the mats identification by combining a first artefact detection step, a tailored clustering algorithm and a region growing algorithm.

Mots clés: Unsupervised Machine Learning; Clustering; Sargassum Detection; Earth Observation Data.

Domaines: Intelligence artificielle [cs.AI]

Estèle Glize, Marie-José Huguet, Marc Lucas, Marion Sutton, Gilles Trédan. Clustering Sargassum Mats from Earth Observation Data. Machine Learning for Earth Observation - MACLEAN 2020, Sep 2020, Ghent, Belgium. ⟨hal-02938183⟩