Oil Spill Detection using Segmentation based Approaches

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/75570
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Title: Oil Spill Detection using Segmentation based Approaches
Authors: Mira Martínez, Damián | Gil, Pablo | Alacid Soto, Beatriz | Torres, Fernando
Research Group/s: Automática, Robótica y Visión Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal
Keywords: Oils Spill Detection | Remote Sensing | Segmentation | Slar Data
Knowledge Area: Ingeniería de Sistemas y Automática
Issue Date: 28-Feb-2017
Publisher: SciTePress
Citation: Mira, D., et al. (2017). “Oil Spill Detection using Segmentation based Approaches”. In: Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017), 442-447. doi:10.5220/0006191504420447
Abstract: This paper presents a description and comparison of two segmentation methods for the oil spill detection in the sea surface. SLAR sensors acquire video sequences from which snapshots are extracted for the detection of oil spills. Both approaches are segmentation based on graph techniques and J-image respectively. Finally, the aim of applying both approaches to SLAR snapshots, as shown, is to detect the largest part of the oil slick and minimize the false detection of the spill.
Sponsor: This work was funded by Ministry of Economy and Competitiveness and supported by Spanish project (RTC-2014-1863-8).
URI: http://hdl.handle.net/10045/75570
ISBN: 978-989-758-222-6
DOI: 10.5220/0006191504420447
Language: eng
Type: info:eu-repo/semantics/conferenceObject
Rights: © 2017 by SCITEPRESS – Science and Technology Publications, Lda.
Peer Review: si
Publisher version: https://doi.org/10.5220/0006191504420447
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