|dc.description.abstract||The analysis and identification of texture is a key area in image processing and computer
vision. One of the most prominent texture analysis algorithms is the Gabor Filter.
These filters are used by convolving an image with a family of self similar filters or
wavelets through the selection of a suitable number of scales and orientations, which
are responsible for aiding in the identification of textures of differing coarseness and
While extensively used in a variety of applications, including, biometrics such as iris and
facial recognition, their effectiveness depend largely on the manual selection of different
parameters values, i.e. the centre frequency, the number of scales and orientations, and
the standard deviations. Previous studies have been conducted on how to determine
optimal values. However the results are sometimes inconsistent and even contradictory.
Furthermore, the selection of the mask size and tile size used in the convolution process
has received little attention, presumably since they are image set dependent.
This research attempts to verify specific claims made in previous studies about the
influence of the number of scales and orientations, but also to investigate the variation of
the filter mask size and tile size for water body extraction from satellite imagery. Optical
satellite imagery may contain texture samples that are conceptually the same (belong
to the same class), but are structurally different or differ due to changes in illumination,
i.e. a texture may appear completely different when the intensity or position of a light
A systematic testing of the effects of varying the parameter values on optical satellite
imagery is conducted. Experiments are designed to verify claims made about the influence of varying the scales and orientations within predetermined ranges, but also to
show the considerable changes in classification accuracy when varying the filter mask
and tile size. Heuristic techniques such as Genetic Algorithms (GA) can be used to find
optimum solutions in application domains where an enumeration approach is not feasible.
Hence, the effectiveness of a GA to automate the process of determining optimum
Gabor filter parameter values for a given image dataset is also investigated.
The results of the research can be used to facilitate the selection of Gabor filter parameters
for applications that involve multi-textured image segmentation or classification,
and specifically to guide the selection of appropriate filter mask and tile sizes for automated
analysis of satellite imagery.||en