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1st International Workshop on Reading Music Systems


Paris, September 20

PRAIg '18 / +pics


11th international workshop on Machine Learning and Music


Stockholm: 13-15th July 2018



  1. Gallego, A.J.; Gil, P.; Pertusa, A.; Fisher, R.B.
    "Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders"
    Sensors, vol. 18, pp. 1424-8220 (2018)
    : bibtex : URL

    In this work, we use deep neural autoencoders to segment oil spills from Side-Looking Airborne Radar (SLAR) imagery. Synthetic Aperture Radar (SAR) has been much exploited for ocean surface monitoring, especially for oil pollution detection, but few approaches in the literature use SLAR. Our sensor consists of two SAR antennas mounted on an aircraft, enabling a quicker response than satellite sensors for emergency services when an oil spill occurs. Experiments on TERMA radar were carried out to detect oil spills on Spanish coasts using deep selectional autoencoders and RED-nets (very deep Residual Encoder-Decoder Networks). Different configurations of these networks were evaluated and the best topology significantly outperformed previous approaches, correctly detecting 100% of the spills and obtaining an F1 score of 93.01% at the pixel level. The proposed autoencoders perform accurately in SLAR imagery that has artifacts and noise caused by the aircraft maneuvers, in different weather conditions and with the presence of look-alikes due to natural phenomena such as shoals of fish and seaweed.

@article {
 author = "Gallego, A.J.; Gil, P.; Pertusa, A.; Fisher, R.B.",
 title  = "Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders",
 issn = "1424-8220",
 journal = "Sensors",
 month = "March",
 number = "3",
 pages = "1424-8220",
 volume = "18",
 year = "2018"
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