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The vector data was overlaid on the image and the target areas exported using the
GDAL export tool to create ten separate .tiff files containing roof values.
The entire image was then loaded into the geomatica software and statistical data
for the red, green and blue primary values (converted to greyscale) obtained. This
was so as to obtain a mean value for these within the entire image, which
contained a range of features including forestry, pasture and several water bodies.
The image was not enhanced and no post processing was used in order to obtain
standard baseline values to measure future iterations of the study against.
It should be noted that the sample area contained 76 buildings and this study
concentrated on the south east of the image (which contained the largest number
of buildings). For each building polygon there was a clear division between
sections of shade and light due to the roof pitch. While there is little value in
pursuing this unique aspect of the image features (as buildings are already being
well captured for mapping purposes by traditional photogrammetry and field
survey), this might be useful for automatic key generation. By this I mean that
pattern recognition software could be applied to separate these two areas and the
resultant variations analyzed to determine the roof pitch; this is, however, beyond
the scope of this study.
Below is a screenshot of the buildings present in the image (so as to give an
impression of their dispersal in the study area);
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