This thesis covers two main areas which are related to the mapping and examination
of the ionosphere. The first examines the performance and specific nuances of
various state-of-the-art interpolation methods with specific application to mapping the
ionosphere. This work forms the most widely scoped examination of interpolation
technique for ionospheric imaging to date, and includes the introduction of normalised
convolution techniques to geophysical data. In this study, adaptive-normalised convolution
was found to perform well in ionospheric electron content mapping, and the
popular technique, kriging was found to have problems which limit its usefulness.
The second, is the development and examination of automatic data-driven motion estimation
methods for use on ionospheric electron content data. Particular emphasis
is given to storm events, during which characteristic shapes appear and move across
the North Pole. This is a particular challenge, as images covering this region tend to
have a very-low resolution. Several motion estimation methods are developed and applied
to such data, including methods based on optical flow, correlation and boundarycorrespondence.
Correlation and relaxation labelling based methods are found to perform
reasonably, and boundary based methods based on shape-context matching are
found to perform well, when coupled with a regularisation stage.
Overall, the techniques examined and developed here will help advance the process
of examining the features and morphology of the ionosphere, both during storms and
|Date of Award||1 Apr 2009|
|Supervisor||Adrian Evans (Supervisor)|
- computer vision
- image processing
- Motion ionosphere