  Ocean surface monitoring, especially oil slick detection, has become
mandatory due to its importance for oil exploration and risk prevention on
ecosystems. For years, the detection task has been performed manually by
photo-interpreters using Synthetic Aperture Radar (SAR) images with the help of
contextual data such as wind. This tedious manual work cannot handle the
increasing amount of data collected by the available sensors and thus requires
automation. Literature reports conventional and semi-automated detection
methods that generally focus either on oil slicks originating from
anthropogenic (spills) or natural (seeps) sources on limited data collections.
As an extension, this paper presents the automation of offshore oil slicks on
an extensive database with both kinds of slicks. It builds upon the slick
annotations of specialized photo-interpreters on Sentinel-1 SAR data for 4
years over 3 exploration and monitoring areas worldwide. All the considered SAR
images and related annotation relate to real oil slick monitoring scenarios.
Further, wind estimation is systematically computed to enrich the data
collection. Paper contributions are the following : (i) a performance
comparison of two deep learning approaches: semantic segmentation using
FC-DenseNet and instance segmentation using Mask-RCNN. (ii) the introduction of
meteorological information (wind speed) is deemed valuable for oil slick
detection in the performance evaluation. The main results of this study show
the effectiveness of slick detection by deep learning approaches, in particular
FC-DenseNet, which captures more than 92% of oil instances in our test set.
Furthermore, a strong correlation between model performances and contextual
information such as slick size and wind speed is demonstrated in the
performance evaluation. This work opens perspectives to design models that can
fuse SAR and wind information to reduce the false alarm rate.
