26 February 2008 Mixing geometric and radiometric features for change classification
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Proceedings Volume 6814, Computational Imaging VI; 681408 (2008); doi: 10.1117/12.777084
Event: Electronic Imaging, 2008, San Jose, California, United States
Abstract
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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Alexandre Fournier, Xavier Descombes, Josiane Zerubia, "Mixing geometric and radiometric features for change classification", Proc. SPIE 6814, Computational Imaging VI, 681408 (26 February 2008); doi: 10.1117/12.777084; https://doi.org/10.1117/12.777084
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KEYWORDS
Buildings

Detection and tracking algorithms

Image classification

Image resolution

Principal component analysis

Target detection

Defense and security

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