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2.3 Spectral Value Comparison
The third part of the algorithm consists of a series of procedures to assign
known areas and areas with values that can be determined from the known sets:
This part of the algorithm involves comparing the spectral values for the unknown
polygon types (from step 2). The first part involves creating statistical histogram
data for all of these polygons and comparing it to an expected value key for
classification according to land use type. Areas which do not conform to known
values are placed in a set for further analysis while those matching are categorized
according to their values. The first part of this step involves verifying any
polygons which were found to include descriptive symbols from the vector data
(marsh, pasture -note: pasture in this case refers to known areas of rough pasture).
Following from this an analysis according to spectral values was completed, and
the set of neighbouring polygons (taken directly from the vector data set)
examined to see if probably neighbouring areas might influence the result. For
example; if an area with a set of pixel values close to those expected to pasture
was identified but displayed a high level of standard deviation this area was given
to the pasture set if three or more neighbouring polygons contained pasture (as the
deviation is probably caused by shade in the image), otherwise the image is
flagged for examination of the histogram results later in the process –to see if a
double spike in the red and green polygons is present.
In order to complete this step the algorithm cycles through a number of relative
values to determine the probable land area of the polygon being analyzed. For this
section of the study the histogram values were exported from the geomatica
software package as tables and graphs and compared manually, in order to
complete the same task on a larger scale this process would be coded into a
routine taking the statistical (image) data and image key as input and outputting
the closest match. For example the mean data by colour band would be compared
to the values for roads and if a 50% decrease in the red, 40% in the green and 50%
in the blue colour bands was detected the polygon would then be compared to
water where if a 70% increase in red, 55% in green and 20% in the blue colour
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