Bedform Parameterization and Object Detection from Sonar Data- Application of Finger Print Algorithms.

摘要

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.