Towards Automatic Detection of Dark Features in the Barents Sea using Synthetic Aperture Radar

Abstract

Increased human presence and commercial activities in the Barents Sea (fishing, offshore oil and gas exploration) are amplifying the need for large-scale operational ocean monitoring of the eventual oil spills in the region. The geographical location and climate impose additional constraints on satellite-based monitoring, making it necessary to use Synthetic Aperture Radar (SAR). Dark features or low backscatter areas are frequent within the SAR images and their occurrence may indicate oil spills or so-called lookalikes. Automatic oil spill detection hinges on accurate separation of the lookalikes from actual oil spills. Two main types exist in the Barents Sea: newly formed sea ice and low wind regions, where the former occur during the freezing part of the year (approx. November - April) and the other year around. Mapping the occurrence of oil spills and lookalikes in the Barents Sea on a seasonal basis would add to our understanding and knowledge of the low backscatter phenomena. Awareness of the major locations of oil spills, natural oil seeps, or lookalikes, are important for operational services and their effort to reduce false alarms. Here, we explore the use of a segmentation-based dark feature detection method with Sentinel-l Extra Wide-Swath SAR images. We test the method on images acquired over the Barents Sea during the freezing season, and cross-validate the results with two sets of dark features segmented by operational expert oil spill and sea ice monitoring services. The results are discussed, together with currently developing method improvements, all while working towards a fully-automated method for monitoring dark features in the Barents Sea.