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flown and satellite imagery. This was not necessary in this thesis due to the clarity
of the photography.
5.2 Vector and polygon based studies of aerial photography
I have labeled this body of knowledge of aerial (and satellite) image processing as
vector and polygon based as the trend that unites the studies is the fact that the
authors sought a pattern or shape based method for extracting the information. As
with spectral (and hybrid spectral and spatial) methods, the underlying cause for
the studies can vary, from understanding Alaskan watercourses (van der Werff &
van der Meer, 2008) to examining the built environment in Moscow (Dudarev,
2009). I did not make use of a particular algorithm or technique from these studies,
but have considered them in this review for their potential in offering a method for
sub dividing small area polygons. It should be noted that there appears to be a
point where pattern analysis is less beneficial, such as the 500pixel minimum
suggested by H. van der Meer and F. van der Meer.
This thesis makes use of building polygons captured from the Irish peri-urban
landscape. These served both as indicators as to the type of land use in the small
area polygon surrounding them (and possibly a spectral control in terms of the
roof tile value). In terms of pattern analysis any automated identification of
modifications or new buildings should recognize the polygon outline as a building.
This is a particular problem in peri-urban areas, in a rural context newly built
slatted sheds etc. will conform to a standard outline but the shapes are more varied
in urban areas. This is particularly so in the peri-urban Irish landscape where one
off housing and a fashion for ugly looking (in terms of aerial analysis) extensions
and sections of building jutting from a main structure mean that establishing a
template pattern for dwellings would be difficult. Outside of considering other
data (such as presence of tarred road etc.) an automated study relying on pattern
identification would need a complicated signature algorithm. This was the basis
for Roman Duradevs 2009 study of building polygon signature point definition. In
this he was considering buildings in context of the city of Moscow, but intended
the algorithm he created for use in a wider variety of data sets. The study tied in
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