Bayesian spatial models for SONAR image interpretation
This thesis is concerned with the utilisation of spatial information in processing of
high-frequency sidescan SONAR imagery, and particularly in how such information
can be used in developing techniques to assist in mapping functions.
Survey applications aim to generate maps of the seabed, but are time consuming
and expensive; automatic processing is required to improve efficiency. Current
techniques have had some success, but utilise little of the available spatial information.
Previously, inclusion of such knowledge was prohibitively expensive; recent
improvements in numerical simulations techniques has reduced the costs involved.
This thesis attempts to exploit these improvements into a method for including spatial
information in SONAR processing and in general to image and signal analysis.
Bayesian techniques for inclusion of prior knowledge and structuring complex
problems are developed and applied to problems of texture segmentation, object detection
and parameter extraction. It is shown through experiments on groundtruth
and real datasets that the inclusion of spatial context can be very effective in improving
poor techniques or, conversely in allowing simpler techniques to be used with
the same objective outcome (with obvious computational advantages). The thesis
also considers some of the implementation problems with the techniques used, and
develops simple modifications to improve common algorithms.