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ship detection & localisation

Ship detection and localisation from satellite images.

Blog posts:

• Ship detection - Part 1: Ship detection, i.e. binary prediction of whether there is at least 1 ship, or not. Part 1 is a simple solution showing great results in a few lines of code
• Ship detection - Part 2: ship detection with transfer learning and decision interpretability through GAP/GMP's implicit localisation properties
• Ship localisation / image segmentation - Part 3: identify where ship are within the image, and highlight pixel by pixel

Part 3 highlight: Image segmentation with a U-Net

Part 2 highlight: Class activation mapping on vessel detection classifier ConvNet - convnet learned where ships are without supervision!

Steps taken:

• find data sets:
  • planet API:
    • needs subscription, but there is a free trial
    • Example usage here: https://medium.com/dataseries/satellite-imagery-analysis-with-python-a06eea5465ea
  • airbus kaggle set (selected for first iteration)
    • https://www.kaggle.com/c/airbus-ship-detection/data
    • to download locally, make sure your connection is stable (29 GB)
      • get API key through your kaggle profile (free), and either save file or enter name and key as environmental variable
      • nohup kaggle competitions download -c airbus-ship-detection & disown %1
  • other data providers: Airbus, Digital Globe
  • free sources: includes EOS's Sentinel 1 (SAR - active/radar) and 2 (optical) with coverage period ranging of 2-7 days https://eos.com/blog/7-top-free-satellite-imagery-sources-in-2019/
• simple EDA on data used in this repo and blog post:
  • 200k+ images of size 768 x 768 x 3
  • 78% of images have no vessel
  • some images have up to 15 vessels
  • Ships within and across images differ in size, and are located in open sea, at docks, marinas, etc.
• modelling broken down into two steps, with subfolders in the repo
  • ship detection: binary prediction of whether there is at least 1 ship, or not
  • ship localisation / image segmentation: identify where ship are within the image, and classify each pixel as having a ship or no ship (alternative could be to have a bounding box, with a different kind of model)
• other articles on the topic:
  • https://www.kaggle.com/iafoss/unet34-dice-0-87/data
  • https://www.kaggle.com/uysimty/ship-detection-using-keras-u-net
  • https://medium.com/dataseries/detecting-ships-in-satellite-imagery-7f0ca04e7964
  • https://github.com/davidtvs/kaggle-airbus-ship-detection
  • https://towardsdatascience.com/deep-learning-for-ship-detection-and-segmentation-71d223aca649
  • https://towardsdatascience.com/u-net-b229b32b4a71
  • https://www.tensorflow.org/tutorials/images/segmentation
  • https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/
  • more classic ship detection algorithms in skimage.segmentation: https://developers.planet.com/tutorials/detect-ships-in-planet-data/