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enchmark would allow the authors to conduct local adjustment for the pixel<br />
values. The study covered 4500 hectares of boreal forest located in the<br />
municipality of Kuru in the south of Finland.<br />
The core of the study is a local radiometric correction method for reducing the<br />
effect of bidirectional reflectance. This problem was not a major issue in the thesis<br />
but the methods used by the authors (finding a larger scale benchmark image to<br />
reference study areas against) was considered.At the heart of the Finnish study the<br />
problem was of similar objects possessing different spectral characteristics in<br />
different parts of the image. This was a problem which was less relevant to the<br />
focus of this study (the authors are focused on forestry data). The authors<br />
conclude, not unsurprisingly, that the value of remote sensing is dependant on<br />
what is visible and what can be registered by the airborne sensor.<br />
As mentioned in the introduction to this chapter, the body of work which<br />
examines automatic capture of hard ground within urban areas is of particular<br />
relevance to this thesis. The 2007 study by Yuyu Zhou and Yu Wang of urban<br />
examples in Rhode Island is a good illustration of the type of factors that need to<br />
be considered in this type of survey. The study, which used true-colour digital<br />
orthophotographic data with a 1m spatial resolution (forming a controlled dataset<br />
in .tiff format with red, green and blue spectral bands present) segmented the<br />
imagery according to urban districts. The authors note that “successful image<br />
segmentation is the most important prerequisite in object-oriented classification”<br />
(P.644). It is hoped that by using previously captured and verified vector data this<br />
thesis will have met this prerequisite.<br />
The algorithm which was employed for this survey was broken down into four<br />
parts; segmentation, compensation for shadow effect, analysis of variance<br />
classification and post classification of the data. There is a large body of work that<br />
has been completed using automatic interpretation of aerial imagery; the focus of<br />
this work is usually towards a specific purpose, such as the 2007 analysis of coffee<br />
crops in Costa Rica outlined by S.Cordero-Snacho and S.Adler. The study is a<br />
useful example of some of the problems that can be encountered when attempting<br />
automatic image analysis. In the study the authors consider the problem of<br />
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