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ased analysis, solely spectral based analysis and a combined “spatial-spectral
classification” (H. van der Weff & F. van der Meer, P.251). These studies are
slightly different from the one being undertaken in this thesis in that the shape and
classification of much of the data will already be known however, the study is
useful to this thesis in that it suggests the potential for a method of identifying new
farm buildings based on a similar classification. The authors are seeking a method
to enhance pixel-based spectral classifications (as will be used in this thesis) by
adding spatial information. It is worth noting that the results of the study were not
satisfactory in terms of automatically correctly identifying features.
The first step the authors used to determine the shape of the areas being examined
was to “seed” (H. van der Meer & F. van der Meer, P.252) the object. This
involved beginning an object with a single pixel of a set value and increasing the
size of the area until a spectral variance in a non-overlapping 3*3 pixel occurs.
This part of the study continued until all the image pixels were segmented into
objects. The authors noted that size was a factor at this point and objects of 500
pixels or less were more successfully determined. The study itself was looking at
parts of Alaska, and the objects being classifies were water bodies; i.e. separating
streams from ox-bow lakes, thaw waters from rivers and sediment rich water. This
is a difficult task due to the relative random nature of these shapes when compared
to a well defined linear pattern that can be observed in the Irish landscape. The
authors conclude (in the case of water bodies) “(that) an object should consist of
approximately 500 pixels at minimum to be able to use the absolute value of shape
measurements” (H. van der Meer & F. van der Meer, P.257).
The authors created a combined analysis method by classifying shapes according
to threes spectral bands from the imagery being used and comparing the results
against the pixel based shape measurements. Using these results they were unable
to distinguish between the water bodies being considered by the study and the
authors suggest that further research is required to better combine the two (shape
and spectral) classifications. In some ways this thesis is a continuation of this, in
that it will be using a spectral analysis in combination with spectral signatures (in
the form of previously captured and coded vector data). The aim the authors had
was to established a means of measurement using an “unbiased software
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