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present in the target area (or its immediate (~1km) surroundings then extracting a
set of baseline pixel values from water polygons would not benefit the analysis. It
can therefore be concluded that water polygons provide a useful reference for
image analysis, but in the context of using them to add value to ordnance survey
small area polygons they need to be part of areas not less than five pixels in
diameter (i.e. belong to classes of rivers, lakes or large ponds).
When the control values for water (from first table) were compared to pasture the
red and green colour bands showed values which could be used to calculate if
pasture was present in a polygon. The mean pixel value for water in the red colour
band was 30% of the value for pasture and only 45% of the value for the green
colour band found in the pasture sample (which included a section of shade). The
value for the blue colour band also had a disparity of just over 20% less than the
pasture mean pixel value. This implies that if an algorithm was to be run on
sections of ordnance survey data which took polygon co-ordinates from the vector
polygons, calibrated a key from the water polygon and compared the red and
green band colour values against the red and green colour values of the
neighbouring polygon and then confirmed the level of standard deviation (which
was found to be low in an area of pasture, ~10 on the greyscale) it is probable the
area can be labelled as pasture. In itself this does not present much of a
breakthrough but when added to the known polygon it helps complete the picture
of a target area being analyzed.
At this point it might be better to think of the image as its vector representation.
As the polygons which the vector data encloses are identified the areas can be
filled. In this way the study is filling the blanks around known values. If the result
was thought of as a mosaic of known area properties (type and nature of land
cover) then applying the label pasture dramatically reduces the areas left to
identify.
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