Full text: Proceedings, XXth congress (Part 7)

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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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