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.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- feature extraction
- image analysis
- sensor fusion