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Open AccessArticle

Semantic Segmentation of SLAR Imagery with Convolutional LSTM Selectional AutoEncoders

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Pattern Recognition and Artificial Intelligence Group, Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain
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Automation, Robotics and Computer Vision Group, Department of Physics, Systems Engineering and Signal Theory, University of Alicante, 03690 Alicante, Spain
3
School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1402; https://doi.org/10.3390/rs11121402
Received: 19 April 2019 / Revised: 11 June 2019 / Accepted: 12 June 2019 / Published: 12 June 2019
(This article belongs to the Special Issue Oil Spill Remote Sensing)
We present a method to detect maritime oil spills from Side-Looking Airborne Radar (SLAR) sensors mounted on aircraft in order to enable a quick response of emergency services when an oil spill occurs. The proposed approach introduces a new type of neural architecture named Convolutional Long Short Term Memory Selectional AutoEncoders (CMSAE) which allows the simultaneous segmentation of multiple classes such as coast, oil spill and ships. Unlike previous works using full SLAR images, in this work only a few scanlines from the beam-scanning of radar are needed to perform the detection. The main objective is to develop a method that performs accurate segmentation using only the current and previous sensor information, in order to return a real-time response during the flight. The proposed architecture uses a series of CMSAE networks to process in parallel each of the objectives defined as different classes. The output of these networks are given to a machine learning classifier to perform the final detection. Results show that the proposed approach can reliably detect oil spills and other maritime objects in SLAR sequences, outperforming the accuracy of previous state-of-the-art methods and with a response time of only 0.76 s. View Full-Text
Keywords: side-looking airborne radar; oil spills; ship detection; coast detection; neural networks; supervised learning side-looking airborne radar; oil spills; ship detection; coast detection; neural networks; supervised learning
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MDPI and ACS Style

Gallego, A.-J.; Gil, P.; Pertusa, A.; Fisher, R.B. Semantic Segmentation of SLAR Imagery with Convolutional LSTM Selectional AutoEncoders. Remote Sens. 2019, 11, 1402.

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