A comparison of three image-object methods for the multiscale analysis of landscape structure |
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Authors: | Geoffrey J Hay Thomas Blaschke Danielle J Marceau Andr Bouchard |
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Institution: | a Geocomputing Laboratory, Département de Géographie, Université de Montréal, C.P. 6128, Succursale Centre-Ville, Montreal, Quebec, Canada H3C 3J;b Department of Geography and Geoinformation, University of Salzburg, Austria, Hellbrunner Str. 34, A-5020, Salzburg, Austria;c IRBV, Université de Montréal, Jardin Botanique de Montréal, 4101 Sherbrooke Est, Montreal, Quebec, Canada H1X 2B2 |
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Abstract: | Within the conceptual framework of Complex Systems, we discuss the importance and challenges in extracting and linking multiscale objects from high-resolution remote sensing imagery to improve the monitoring, modeling and management of complex landscapes. In particular, we emphasize that remote sensing data are a particular case of the modifiable areal unit problem (MAUP) and describe how image-objects provide a way to reduce this problem. We then hypothesize that multiscale analysis should be guided by the intrinsic scale of the dominant landscape objects composing a scene and describe three different multiscale image-processing techniques with the potential to achieve this. Each of these techniques, i.e., Fractal Net Evolution Approach (FNEA), Linear Scale-Space and Blob-Feature Detection (SS), and Multiscale Object-Specific Analysis (MOSA), facilitates the multiscale pattern analysis, exploration and hierarchical linking of image-objects based on methods that derive spatially explicit multiscale contextual information from a single resolution of remote sensing imagery. We then outline the weaknesses and strengths of each technique and provide strategies for their improvement. |
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Keywords: | complex systems theory fractal net evolution approach image-objects multiscale object-specific analysis |
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