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The Sentinel-1 Model is based upon the classic object detection model, Faster R-CNN, which is a two stage detector made up of a Region Proposal Network (RPN) and a classification head. The RPN’s job is to generate proposals, where there is likely an object of interest, and the classification head grabs the most confident of these proposals and predicts scores (such as is_vessel and is_fishing) for each proposal.
The model detects vessels and predicts vessel attributes from Sentinel-1 SAR images. In particular, it uses the dual polarization mode (VV + VH) of the Interferometric Wide swath (IW) acquisition mode of Sentinel-11, and produces point detections of vessels, cropped outputs surrounding those detections, and attributes associated with the detected vessel; currently estimated length is displayed in Skylight).
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The current version of the model has been trained on Sentinel-1 scenes from several geographic areas that were annotated by hand by subject matter experts. A total of 55,499 point labels were used. These areas include water bodies around Bahamas, Ghana, Madagascar, Persian Gulf, Argentina, Taiwan, and Singapore. are distributed globally and shown in the image below.
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The model predicts the positions of vessels, and assigns each prediction a confidence score. Detections with a confidence score > .9 are displayed in Skylight. The other attribute that the model predicts is vessel length.
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