Mixing Geometric and Radiometric Features for Change Classification - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2008

Mixing Geometric and Radiometric Features for Change Classification

Alexandre Fournier
  • Function : Author
  • PersonId : 848149
Xavier Descombes
Josiane Zerubia
  • Function : Author
  • PersonId : 833424


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.
Fichier principal
Vignette du fichier
inria-00269853.pdf (826.92 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

inria-00269853 , version 1 (03-04-2008)


  • HAL Id : inria-00269853 , version 1


Alexandre Fournier, Xavier Descombes, Josiane Zerubia. Mixing Geometric and Radiometric Features for Change Classification. SPIE, Electronic Imaging, Jan 2008, San Jose, United States. ⟨inria-00269853⟩
123 View
134 Download


Gmail Facebook X LinkedIn More