An approach for SLAR images denoising based on removing regions with low visual quality for oil spill detection

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/75571
Información del item - Informació de l'item - Item information
Title: An approach for SLAR images denoising based on removing regions with low visual quality for oil spill detection
Authors: Alacid Soto, Beatriz | Gil, Pablo
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: SLAR | Oil spill | Segmentation | Detection | Denoising | Saliency map
Knowledge Area: Ingeniería de Sistemas y Automática
Issue Date: 18-Oct-2016
Publisher: SPIE, The International Society for Optics and Photonics
Citation: Beatriz Alacid, Pablo Gil, "An approach for SLAR images denoising based on removing regions with low visual quality for oil spill detection", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 1000419 (18 October 2016); doi:10.1117/12.2239257
Abstract: This paper presents an approach to remove SLAR (Side-Looking Airborne Radar) image regions with low visual quality to be used for an automatic detection of oil slicks on a board system. This approach is focused on the detection and labelling of SLAR image regions caused by a poor acquisition from two antennas located on both sides of an aircraft. Thereby, the method distinguishes ineligible regions which are not suitable to be used on the steps of an automatic detection process of oil slicks because they have a high probability of causing false positive results in the detection process. To do this, the method uses a hybrid approach based on edge-based segmentation aided by Gabor filters for texture detection combined with a search algorithm of significant grey-level changes for fitting the boundary lines in each of all the bad regions. Afterwards, a statistical analysis is done to label the set of pixels which should be used for recognition of oil slicks. The results show a successful detection of the ineligible regions and consequently how the image is partitioned in sub-regions of interest in terms of detecting the oil slicks, improving the accuracy and reliability of the oil slick detection.
Sponsor: This work was supported by the project (RTC-2014-1863-8) of call for collaboration challenges MINECO.
URI: http://hdl.handle.net/10045/75571
ISBN: 9781510604124
ISSN: 0277-786X | 1996-756X (Online)
DOI: 10.1117/12.2239257
Language: eng
Type: info:eu-repo/semantics/conferenceObject
Rights: Copyright 2016 Society of Photo-Optical Instrumentation Engineers. This paper was published in Proceedings of SPIE, vol. 10004, and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Peer Review: si
Publisher version: https://doi.org/10.1117/12.2239257
Appears in Collections:INV - AUROVA - Comunicaciones a Congresos Internacionales

Files in This Item:
Files in This Item:
File Description SizeFormat 
Thumbnail2016_Alacid_Gil_ProcSPIE.pdf716,81 kBAdobe PDFOpen Preview


Items in RUA are protected by copyright, with all rights reserved, unless otherwise indicated.