Reconstruction and motion estimation of sparsely sampled ionospheric data

  • Matthew Foster

Student thesis: Doctoral ThesisPhD

Abstract

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 quiet times.
Date of Award1 Apr 2009
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorAdrian Evans (Supervisor)

Keywords

  • computer vision
  • image processing
  • interpolation
  • Motion ionosphere

Cite this

Reconstruction and motion estimation of sparsely sampled ionospheric data
Foster, M. (Author). 1 Apr 2009

Student thesis: Doctoral ThesisPhD