| Summary: | This thesis addresses the problem of computationally estimating the motion of pack ice in sequential digital satellite images. The problem is posed in terms of linear filter theory and is solved by minimizing the error variance. The intuitive use of cross correlation and edge detection are shown to flow naturally from this approach. The theoretical framework also allows a geometric intuition into the action of the filter which is not possible through ad hoc methods. The noise corrupting the filtering process is investigated and the filter is implemented through both a first order method common to image processing, and a more sophisticated second order approach from computational vision. The class of imagery for which the filtering system is appropriate is discussed and the images chosen for the experiments are shown to be representative of this class. The experimental results reveal the power of the system in estimating ice motion, and some analysis of the derived motion is performed by comparison to a simple theory of wind-driven ice motion. The failings of the system are discussed and improvements are suggested. Science, Faculty of Earth, Ocean and Atmospheric Sciences, Department of Graduate
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