Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders
AbstractIn 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
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Gallego, A.-J.; Gil, P.; Pertusa, A.; Fisher, R.B. Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders. Sensors 2018, 18, 797.
Gallego A-J, Gil P, Pertusa A, Fisher RB. Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders. Sensors. 2018; 18(3):797.Chicago/Turabian Style
Gallego, Antonio-Javier; Gil, Pablo; Pertusa, Antonio; Fisher, Robert B. 2018. "Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders." Sensors 18, no. 3: 797.
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