Intensity Normalization of Sidescan Sonar Imagery

Al-Ravi, M.S. and Galdrán, Adrián and Yuan, Xin and Eckert, Martina and Martínez Ortega, José Fernán and Elmgrem, Fredrik and Cürüklü, Baran and Rodríguez, Jonathan and Bastos, Joaquim and Pinto, Marc (2016). Intensity Normalization of Sidescan Sonar Imagery. In: "Image Processing Theory Tools and Applications (IPTA), 2016 6th International Conference on", 12-15 Dec. 2016, Oulu, Finlandia. pp. 1-6. https://doi.org/10.1109/IPTA.2016.7820967.

Description

Title: Intensity Normalization of Sidescan Sonar Imagery
Author/s:
  • Al-Ravi, M.S.
  • Galdrán, Adrián
  • Yuan, Xin
  • Eckert, Martina
  • Martínez Ortega, José Fernán
  • Elmgrem, Fredrik
  • Cürüklü, Baran
  • Rodríguez, Jonathan
  • Bastos, Joaquim
  • Pinto, Marc
Item Type: Presentation at Congress or Conference (Article)
Event Title: Image Processing Theory Tools and Applications (IPTA), 2016 6th International Conference on
Event Dates: 12-15 Dec. 2016
Event Location: Oulu, Finlandia
Title of Book: 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA)
Date: 2016
Subjects:
Faculty: Centro de Investigación en Tecnologías Software y Sistemas Multimedia para la Sostenibilidad (CITSEM) (UPM)
Department: Ingeniería Telemática y Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Sonar imaging is currently the exemplary choice used in underwater imaging. However, since sound signals are absorbed by water, an image acquired by a sonar will have gradient illumination; thus, underwater maps will be difficult to process. In this work, we investigated this phenomenon with the objective to propose methods to normalize the images with regard to illumination. We propose to use MIxed exponential Regression Analysis (MIRA) estimated from each image that requires normalization. Two sidescan sonars have been used to capture the seabed in Lake Vattern in Sweden in two opposite directions west-east and east-west; hence, the task is extremely difficult due to differences in the acoustic shadows. Using the structural similarity index, we performed similarity analyses between corresponding regions extracted from the sonar images. Results showed that MIRA has superior normalization performance. This work has been carried out as part of the SWARMs project (http://www.swarms.eu/).

Funding Projects

TypeCodeAcronymLeaderTitle
Horizon 2020662107SWARMsUniversidad Politécnica de MadridSmart and Networking UnderWAter Robots in Cooperation Meshes
Government of SpainTEC2013-48453-C2-2-RUnspecifiedUniversidad Politécnica de MadridMixed reality over ultra high definition television

More information

Item ID: 46639
DC Identifier: http://oa.upm.es/46639/
OAI Identifier: oai:oa.upm.es:46639
DOI: 10.1109/IPTA.2016.7820967
Official URL: https://ieeexplore.ieee.org/document/7820967
Deposited by: Memoria Investigacion
Deposited on: 11 Dec 2018 12:55
Last Modified: 11 Dec 2018 12:55
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