Deep learning applied to sea surface semantic segmentation: Filtering sunglint from aerial imagery (Proposals Track)
Teodor Vrecica (UCSD); Quentin Paletta (University of Cambridge); Luc Lenain (UCSD)
Abstract
Water waves are an ubiquitous feature of the oceans, which serve as a pathway for interactions with the atmosphere. Wave breaking in particular is crucial in developing better understanding of the exchange of momentum, heat, and gas fluxes between the ocean and the atmosphere. Characterizing the properties of wave breaking using orbital or suborbital imagery of the surface of the ocean can be challenging, due to contamination from sunglint, a persistent feature in certain lighting conditions. Here we propose a supervised learning approach to accurately detect whitecaps from airborne imagery obtained under a broad range of lighting conditions. Finally, we discuss potential applications for improving ocean and climate models.