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algorithm” (H. van der Meer & F. van der Meer, P.257), this would seem difficult
to achieve in the case of a relatively chaotic Alaskan wilderness but might be
better applied to peri-urban land parcels.
Another example of a study which combines a number of different aspects of
remote sensing to analyze aerial data in the 2007 random field model for urban
area detection developed by Ping Zhong and Runsheng. In this study the authors
presented a method for interpreting remote images of urban environments that
makes use of what they call “conditional random fields” (Zhong and Wang, 2007).
The study is a response to the fact that although considerable research has been
completed on land cover analysis, the algorithms generally adapt for only a
narrow range of image resolutions and therefore only a few types of urban areas.
They see previous attempts at urban analysis as being based on either gray-level-
based spectral analysis or using texture descriptors. They further note that edge
strength measures can be used to extract homogenous regions. This is an
interesting concept, and may have an application in the automatic capture of large
utility features in rural areas, such as silage pits.
The authors establish a discriminative method for identifying regions in the
photography based on interactions with the neighboring regions. This allows them
to utilize the conditional random fields in terms of context to identify areas. The
authors broke this technique into the jobs of configuring the features, selecting
classifications and classifier fusion. The proposed algorithm compares the fields
against the data segments and places them in a classified segmented model; the
authors compare their results against two previous algorithms, Stacked Feature
Based (where a number of different feature types a re concatenated into one model)
and Straight Line Statistics (where areas of high incidence are used to identify
urban areas). They observed a higher output rate against the first method (based
on time on a 2.4Dhz Pentium machine) and decreased accuracy in detecting
smaller rural areas against the second (where straight line statistics were not
effective against urban areas smaller than 400*400pixels). The method the authors
use, of allowing each component part of the search to train based on “its own
aspects” (Zhong & Wang, 2007) appeared to give positive results against the 60
training and 91 test images used, and was able to successfully identify blocks of
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