Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders
Empreu sempre aquest identificador per citar o enllaçar aquest ítem
http://hdl.handle.net/10045/74110
Títol: | Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders |
---|---|
Autors: | Gallego, Antonio-Javier | Gil, Pablo | Pertusa, Antonio | Fisher, Robert B. |
Grups d'investigació o GITE: | Reconocimiento de Formas e Inteligencia Artificial | Automática, Robótica y Visión Artificial |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Paraules clau: | Oil spill detection | Side-looking airborne radar | Neural networks | Supervised learning | Radar detection |
Àrees de coneixement: | Lenguajes y Sistemas Informáticos | Ingeniería de Sistemas y Automática |
Data de publicació: | 6-de març-2018 |
Editor: | MDPI |
Citació bibliogràfica: | 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. doi:10.3390/s18030797 |
Resum: | 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. |
Patrocinadors: | This work was funded by both the Spanish Government’s Ministry of Economy, Industry and Competitiveness and Babcock MCS Spain through the RTC-2014-1863-8 and INAER4-14Y(IDI-20141234) projects as well as by the grant number 730897 under the HPC-EUROPA3 project, a Research and Innovation Action supported by the European Commission’s Horizon 2020 programme. |
URI: | http://hdl.handle.net/10045/74110 |
ISSN: | 1424-8220 |
DOI: | 10.3390/s18030797 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/article |
Drets: | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Revisió científica: | si |
Versió de l'editor: | http://dx.doi.org/10.3390/s18030797 |
Apareix a la col·lecció: | Investigacions finançades per la UE INV - GRFIA - Artículos de Revistas INV - AUROVA - Artículos de Revistas |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
---|---|---|---|---|
2018_Gallego_etal_Sensors.pdf | 4,04 MB | Adobe PDF | Obrir Vista prèvia | |
Aquest ítem està subjecte a una llicència de Creative Commons Llicència Creative Commons