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This paper introduces a multi-temporal image processing framework towards an efficient and (semi-) automated detection of urban changes. Nonlinear scale space filtering was embedded in an object-based classification procedure and the resulted simplified images provided a more compact and reliable source in order to generate image objects in various scales.(More)
In this paper an object-based classification framework is introduced for the automated monitoring of changes in urban areas. Morphological scale space filtering is embedded in the processing procedure constraining qualitatively the multi-level segmentation and thus, the structure of object hierarchy. The elegantly simplified images provide a more compact(More)
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