International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
contrast boundaries between Lawns and Forest areas
resulted in these regions being merged into a single
patch. The confusion between Building and Road was
not a result of segmentation as generally these two
classes were well delineated. However, confusion
occurred because the spectral radiances of the two
classes were sometimes very similar. This arises
because materials such as asphalt, stone and concrete
are used for both building roofs and roads.
As part of the classifications carried out using
maximum likelihood, all pixels were assigned to the
class with the highest likelihood. This is a relative,
not an absolute measure. Thus even classes that result
in very low likelihood when compared to all the
training data sets are classified. It is possible that a
region is not represented by any of the training data
sets, and this should be identified. In future work, it
may be desirable to establish an absolute minimum
maximum likelihood for classification. Patches that
fail to meet the minimum value would be flagged as
unknown.
6. CONCLUIONS
This study produced a region-based classification
approach specifically designed for high spatial
resolution imagery. The new classification method
resulted in improved results at both the image object
scale and a richer attribution at the aggregate land cover
scale. This research made a contribution to the growing
field of analysis of high spatial resolution imagery.
The methods developed in this research are important
not just because they produce more accurate results that
show the spatial patterns more clearly because of their
lack of distracting high frequency noise. The
delineation and attribution of image objects, rather than
classified pixels, is an important step toward integrating
remote sensing with GIS. The object-based approach
resulted in a pleasing simplicity of spatial structure
compared to the noisy patterns of traditional pixel-
based classification.
Acknowledgements
This Project was funded by the Ministry of Science and
Technology, Republic of Korea.
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