Multisensor integration for underwater scene classification

N. Nandhakumar, S. Malik

Research output: Contribution to journalArticle

Abstract

We describe a new approach for the classification of a seafloor that is imaged with high frequency sonar and optical sensors. Information from these sensors is combined to evaluate the material properties of the seafloor. Estimation of material properties is based on the phenomenological relationship between the acoustical image intensity, surface roughness, and intrinsic object properties in the underwater scene. The sonar image yields backscatter estimates, while the optical stereo imagery yields surface roughness parameters. These two pieces of information are combined by a composite roughness model of high-frequency bottom backscattering phenomenon. The model is based on the conservation of acoustic energy travelling across a fluid-fluid interface. The model provides estimates of material density ratio and sound velocity ratio for the seafloor. These parameters serve as physically meaningful features for classification of the seafloor. Experimental results using real data illustrate the usefulness of this approach for autonomous and/or remotely operated undersea activity.

LanguageEnglish (US)
Pages207-216
Number of pages10
JournalApplied Intelligence
Volume5
Issue number3
DOIs
StatePublished - Jul 1 1995

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Surface roughness
Sonar
Materials properties
Fluids
Optical sensors
Acoustic wave velocity
Backscattering
Conservation
Acoustics
Sensors
Composite materials

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

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abstract = "We describe a new approach for the classification of a seafloor that is imaged with high frequency sonar and optical sensors. Information from these sensors is combined to evaluate the material properties of the seafloor. Estimation of material properties is based on the phenomenological relationship between the acoustical image intensity, surface roughness, and intrinsic object properties in the underwater scene. The sonar image yields backscatter estimates, while the optical stereo imagery yields surface roughness parameters. These two pieces of information are combined by a composite roughness model of high-frequency bottom backscattering phenomenon. The model is based on the conservation of acoustic energy travelling across a fluid-fluid interface. The model provides estimates of material density ratio and sound velocity ratio for the seafloor. These parameters serve as physically meaningful features for classification of the seafloor. Experimental results using real data illustrate the usefulness of this approach for autonomous and/or remotely operated undersea activity.",
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Multisensor integration for underwater scene classification. / Nandhakumar, N.; Malik, S.

In: Applied Intelligence, Vol. 5, No. 3, 01.07.1995, p. 207-216.

Research output: Contribution to journalArticle

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