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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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 11; no. 12; p. 1402
Main Authors: Gallego, Antonio-Javier, Gil, Pablo, Pertusa, Antonio, Fisher, Robert B.
Format: Journal Article
Language:English
Published: Basel MDPI AG 12.06.2019
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs11121402