Most basic change detection algorithms use a pixel-based approach. Whereas such approach is quite well defined
for monitoring important area changes (such as urban growth monitoring) in low resolution images, an object
based approach seems more relevant when the change detection is specifically aimed toward targets (such as
small buildings and vehicles).
In this paper, we present an approach that mixes radiometric and geometric features to qualify the changed
zones. The goal is to establish bounds (appearance, disappearance, substitution ...) between the detected changes
and the underlying objects. We proceed by first clustering the change map (containing each pixel bitemporal
radiosity) in different classes using the entropy-kmeans algorithm. Assuming that most man-made objects have
a polygonal shape, a polygonal approximation algorithm is then used in order to characterize the resulting zone
shapes. Hence allowing us to refine the primary rough classification, by integrating the polygon orientations in
the state space. Tests are currently conducted on Quickbird data.
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