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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
compared with those from single-resolution approaches. Multi-
scale data analysis can provide useful information to ensure that
subsequent classification methods and parameters are suited to
the spatial characteristics of the features (or classes). The re-
sults confirm the validity and efficiency of the proposed
framework.
This research is an initial step towards building an integrated
multi-resolution image analysis and classification framework
for land use/cover mapping using multiple spatial resolution
and multispectral earth observation data. Further test in using
real satellite data with different spatial resolutions will be con-
ducted in different landscapes. The refinement, particularly of
class structures and descriptors from spatial techniques, and ex-
ploration of how different spatial techniques can quantify reso-
lution-dependent spatial characteristics of the image and can be
used in the classification routine are required. More advanced
classification approaches such as neural nets, fuzzy set classifi-
ers, and expert classifier models will also be tested in the multi-
resolution context in the future. Further research in multi-
resolution error modeling to linking classification results across
different spatial resolutions is also required.
ACKNOWLEDGEMENTS
This research is supported by Academic Research Committee,
Queen's University and the National Science and Engineering
Research Council of Canada.
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