This thesis examines the application of scientific visualisation to the analysis of
polarimetric radar data sets. The research contained herein forms part of a larger
body of work that studies the application of scientific visualisation to the analysis of
large multi-valued datasets.
Visualisation techniques have historically assumed a fundamental role in the analysis
of patterns in geographic datasets. This is particularly apparent in the analysis of
remotely sensed data, which, since the advent of aerial photography, has utilised the
intensity of visible (and invisible) electromagnetic energy as a means of producing
synoptic map-like images.
Progress in remote sensing technology, however, has led to the development of
systems which measure very large numbers of intensity 'channels', or require the
analysis of variables other than intensity values. Current visualisation strategies are
insufficient to adequately represent such datasets, whilst retaining the synoptic
perspective.
In response to this, two new visualisation techniques are presented for the analysis of
polarimetric radar data. Both techniques demonstrate how it is possible to produce
synoptic image suitable for the analysis of spatial patterns without relying on pixel based
intensity images. This allows a large number of variables to be ascribed to a
single geographic location, and thus encourages the rapid identification of patterns
and anomalies within datasets. The value of applying the principals of scientific
visualisation to exploratory data analysis is subsequently demonstrated with
reference to a number of case studies that highlight the potential of the newly
developed techniques
