Semantic Segmentation of SLAR Imagery with Convolutional LSTM Selectional AutoEncoders

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...

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Vydané v:	Remote sensing (Basel, Switzerland) Ročník 11; číslo 12; s. 1402
Hlavní autori:	Gallego, Antonio-Javier, Gil, Pablo, Pertusa, Antonio, Fisher, Robert B.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 12.06.2019
Predmet:	Airborne radar Airborne sensing Aircraft Artificial intelligence coast detection coasts Computer architecture Emergency response flight Image detection Image processing Image segmentation International conferences Learning algorithms Long short-term memory Machine learning Methods Neural networks Object recognition Oil spills Radar Remote sensing Response time Satellites Semantic segmentation Semantics Sensors Sequences ship detection ships side-looking airborne radar supervised learning Time response United States > US Germany Spain
ISSN:	2072-4292, 2072-4292
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