1 - Conditional mixed-state model for structural change analysis from very high resolution optical images.
B. Belmudez et V. Prinet et J.F. Yao et P. Bouthemy et X. Descombes. Dans Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, juillet 2009.
Mots-clés : Change detection, mixed Markov models.
@INPROCEEDINGS{bel09,
|
| author |
= |
{Belmudez, B. and Prinet, V. and Yao, J.F. and Bouthemy, P. and Descombes, X.}, |
| title |
= |
{Conditional mixed-state model for structural change analysis from very high resolution optical images}, |
| year |
= |
{2009}, |
| month |
= |
{juillet}, |
| booktitle |
= |
{IGARSS}, |
| address |
= |
{Cape Town, South Africa}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00398062/}, |
| keyword |
= |
{Change detection, mixed Markov models} |
| } |
Abstract :
The present work concerns the analysis of dynamic scenes from earth observation images. We are interested in building a map which, on one hand locates places of change, on the other hand, reconstructs a unique visual information of the non-change areas. We show in this paper that such a problem can naturally be takled with conditional mixed-state random field modeling (mixed-state CRF), where the ”mixed state” refers to the symbolic or continous nature of the unknown variable. The maximum a posteriori (MAP) estimation of the CRF is, through the Hammersley-Clifford theorem, turned into an energy minimisation problem. We tested the model on several Quickbird images and illustrate the quality of the results. |
|
haut de la page
Rapport d'activité 2011
bibtex
