Research Project Title
Bedform Parameterization and Object Detection From Sonar Data- Application of Finger Print Algorithms
The long-range goals of this research are to improve our ability to characterize the seabed geometry and texture in energetic inner-shelf/bay mouth settings composed of heterogeneous sedimentary material and possessed of dynamic seabed ripples. Our purpose is to improve our understanding of bedform dynamics and spatio-temporal length scales and defect densities through the application of a recently developed finger print algorithm technique (Skarke and Trembanis, 2011) in the vicinity of manmade seabed objects and dynamic natural ripples on the inner shelf utilizing high-resolution swath sonar collected by an AUV and from surface vessel sonars in energetic coastal settings with application to critical military operations such as mine countermeasures.
Other UD Faculty
Carter DuVal, Trevor Metz
Larry Mayer (UNH) and Jonathan Beaudoin (UNH)
Relevant publications/products related to project
Skarke, A., and A.C. Trembanis, 2011. Parameterization of bedform morphology and defect density with fingerprint analysis techniques. Continental Shelf Research, 31: 1688-1700.