Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation
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http://hdl.handle.net/10045/72317
Título: | Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation |
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Autor/es: | Gil, Pablo | Alacid Soto, Beatriz |
Grupo/s de investigación o GITE: | Automática, Robótica y Visión Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Maritime surveillance | Oil spill detection | Side-Looking Airborne Radar | Radar detection |
Área/s de conocimiento: | Ingeniería de Sistemas y Automática |
Fecha de publicación: | 8-ene-2018 |
Editor: | MDPI |
Cita bibliográfica: | Gil P, Alacid B. Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation. Sensors. 2018; 18(1):151. doi:10.3390/s18010151 |
Resumen: | This work presents a method for oil-spill detection on Spanish coasts using aerial Side-Looking Airborne Radar (SLAR) images, which are captured using a Terma sensor. The proposed method uses grayscale image processing techniques to identify the dark spots that represent oil slicks on the sea. The approach is based on two steps. First, the noise regions caused by aircraft movements are detected and labeled in order to avoid the detection of false-positives. Second, a segmentation process guided by a map saliency technique is used to detect image regions that represent oil slicks. The results show that the proposed method is an improvement on the previous approaches for this task when employing SLAR images. |
Patrocinador/es: | This work was supported by the Spanish Ministry of Economy and Competitiveness through the ONTIME research project (RTC-2014-1863-8). |
URI: | http://hdl.handle.net/10045/72317 |
ISSN: | 1424-8220 |
DOI: | 10.3390/s18010151 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 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/). |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.3390/s18010151 |
Aparece en las colecciones: | INV - AUROVA - Artículos de Revistas INV - HURO - Artículos de Revistas |
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