View/Open - ARAN

Aerial imagery is a form of database. Once it is structured into tables it can be
interrogated for spectral properties just like vector data in a spatial database. By
using the boundary data points from the vector data the imagery is converted to
manageable sections and properties can be determined and logged. This sub
division of the image into a mosaic of areas, starting with known polygons,
moving to polygons which can be easily classified (strong variation form the other
known values with a low level of standard deviation such as cut pasture) and
flagging any whose values fall outside the image key means the job of analyzing
the image is made easier. This sub-dividing of raster imagery is something which
has not been attempted with Irish ordnance data and aerial photography (to the
best of my knowledge, I have conducted a search of research papers and similar
work had not been undertaken within the ordnance survey). The focus of the study
is on proving that this method is practical, and can be applied to a variety of area
types. The methods suggested by this study are unique to the area divisions and
available vector data, and present the steps necessary to train an image key to look
for specific properties in the Irish landscape.
The process works by taking the point data from the polygons contained within
vector data representing an area of an image. Using this point data to crop the area
of the image the polygon it represents and log pixel values for that area. This is
repeated for every area in the region of interest. These are then compared to an
image key and areas classified according to the presence of values specific to the
key. One such key was developed during this thesis but could be re-calibrated for
higher values. In other words a higher mean for an water bodies within a separate
run of photography would increase the key values by that amount in the key.
Within the key are known values (water, forestry, roads etc.) and the proportional
difference (in terms of the mean pixel count for values in the red, green and blue
colour bands, and the levels of standard deviation) between these known values
and search values (such as pasture) is measured against the histogram values for
cropped polygons of unknown use and a category applied for matches. In other
words the process steps through locating, cutting and analyzing small areas of the
image to enhance the available data and search specific values across the whole
image.
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