Ship velocity estimation in SAR images using multitask deep learning

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SAR satellites are used for monitoring ships worldwide. Moving ships are Doppler shifted by an amount proportional to their velocity. An offset between the ship and its wake is then produced during SAR processing. We present a novel automatic method for calculating the ship velocity. The method relies on multitask deep learning to estimate the offset and ship heading. From these parameters, the ship velocity can be obtained. A convolutional neural network is trained using a coupled loss function. The loss function allows both parameters to be estimated at the same time. We show the methods’ effectiveness for ships in Sentinel-1 SAR images. For this purpose, a large dataset of 30,000 AIS annotated SAR ship images is collected. These images have 20 × 22 m pixel resolution and ships do not have a clear wake. The AIS provides the true ship velocity and allows the method to be evaluated. As a result, we can determine the ship speed with an accuracy of 1.1 m/s. The offset disappears near the azimuth direction of the SAR image. Yet, our method is reliable except for ships sailing within 2.5 degrees of the azimuth direction.
Original languageEnglish
Article number113492
JournalRemote Sensing of Environment
Number of pages10
Publication statusPublished - 2023


  • Remote sensing
  • Synthetic Aperture Radar
  • SAR
  • Deep learning
  • CNN
  • Convolutional neural network
  • Multitask learning
  • Ship
  • Velocity
  • Doppler offset
  • Azimuth offset
  • Course
  • Heading


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