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Shade test sample 3
(Pasture containing
little shade)
Mean pixel value Standard deviation
Red 123.828 8.645
Green 177.68 9.8
Blue 105.261 12.69
Table 18: Shade test sample value 3
The sampling for shade highlighted the need for any automatic aerial image
algorithm to account for levels of standard deviation; and also the necessity to
detect peaks of values within the colour bands. The two peaks for lower and
higher values in the second test sample demonstrated the usefulness of
categorizing the images according to spectral values and is a good example of how,
once the imagery can be broken into its constituent area polygons using vector
mapping, useful data can be derived. The properties of shade in the sample
imagery were uniform, allowing the distortion caused by shade to the spectral
values of the target polygons to be included into image processing. The results of
this part of the study go some way to proving the central premise of this thesis; it
is possible to automatically scan aerial imagery using good quality vector data.
The process of eliminating known areas and flagging borderline values (such as
the distortion in the first test sample) means that the land cover can be examined
through a series of scans until all the surface area is accounted for. This is a
process which could then be adapted for specific projects (flood mapping etc.).
The software process outlined here would act as a template for these projects and
allow the users to input specific search values themselves without relying on
obtaining the data from previous studies.
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