Towards real-time sea-floor surface reconstruction and classification using 3-D side-scan sonar

Resource type
Thesis type
(Thesis) M.A.Sc.
Date created
This thesis presents a computer algorithm to solve two major hurdles for generating real-time automated sea-floor maps with composition classification using 3-D side-scan sonar data. The algorithm consists of two distinct parts: sea-floor profiling and sea-flooring classification with computation acceleration from a graphics processing unit (GPU). The sea-floor profiling algorithm is an automated method that identifies bathymetry data corresponding to the sea-floor while ignoring bathymetry corresponding to water column objects and multi-path returns. The algorithm improves upon a fuzzy curve tracing method to handle discontinuities in the point-cloud data along the sea-floor and to discriminate between the sea-floor and other data. With an average error of 2.6% and a computation time of 7.40ms, the sea-floor profiling algorithm is extremely accurate and efficient. Classification of the sea-floor regions consists of applying image texture methods and machine learning classifiers to side-scan sonar images. In this thesis, a feature space for each side-scan sonar image pixel is created using image texture analysis algorithms, and classified with an artificial neural network. The accuracy and performance of the algorithm is tested with side-scan sonar images from the Underwater Research Lab's Pam Rocks sonar survey. Real-time classification was achieved by the use of GPU computing. Porting the algorithm onto the GPU using OpenCL reduced the per-ping computation time to an average of 100ms, with an average error of 3.4%, making it a viable real-time solution in a sonar system.
Copyright statement
Copyright is held by the author.
The author granted permission for the file to be printed and for the text to be copied and pasted.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Bird, John
Member of collection
Attachment Size
etd8470_RGoldade.pdf 17.67 MB