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Publications de Nicolas Baghdadi
Résultat de la recherche dans la liste des publications :
2 Articles |
1 - Comparative study on the performance of multi paramater SAR data for operational urban areas extraction.
C. Corbane et N. Baghdadi et X. Descombes et M. Petit. IEEE-Geoscience and Remote Sensing Letters, 6(4): pages 728-732, octobre 2009.
Mots-clés : Markov random field model, synthetic aperture radar, urban remote sensing.
@ARTICLE{COR-09,
|
| author |
= |
{Corbane, C. and Baghdadi, N. and Descombes, X. and Petit, M.}, |
| title |
= |
{Comparative study on the performance of multi paramater SAR data for operational urban areas extraction}, |
| year |
= |
{2009}, |
| month |
= |
{octobre}, |
| journal |
= |
{IEEE-Geoscience and Remote Sensing Letters}, |
| volume |
= |
{6}, |
| number |
= |
{4}, |
| pages |
= |
{728-732}, |
| url |
= |
{http://dx.doi.org/10.1109/LGRS.2009.2024225}, |
| keyword |
= |
{Markov random field model, synthetic aperture radar, urban remote sensing} |
| } |
Abstract :
The advent of a new generation of synthetic aperture radar (SAR) satellites, such as Advanced SAR/Environmental Satellite (C-band), Phased Array Type L-band Synthetic Aperture Radar/Advanced Land Observing Satellite (L-band), and TerraSAR-X (X-band), offers advanced potentials for the detection of urban tissue. In this letter, we analyze and compare the performance of multiple types of SAR images in terms of band frequency, polarization, incidence angle, and spatial resolution for the purpose of operational urban areas delineation. As a reference for comparison, we use a proven method for extracting textural features based on a Gaussian Markov Random Field (GMRF) model. The results of urban areas delineation are quantitatively analyzed allowing performing intrasensor and intersensors comparisons. Sensitivity of the GMRF model with respect to texture window size and to spatial resolutions of SAR images is also investigated. Intrasensor comparison shows that polarization and incidence angle play a significant role in the potential of the GMRF model for the extraction of urban areas from SAR images. Intersensors comparison evidences the better performances of X-band images, acquired at 1-m spatial resolution, when resampled to resolutions of 5 and 10 m. |
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2 - Extraction automatique des réseaux linéiques à partir d'images satellitaires et aériennes par processus Markov objet.
C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Bulletin de la Société Française de Photogrammétrie et de Télédétection, 170: pages 13--22, 2003.
@ARTICLE{lacostesfpt,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Extraction automatique des réseaux linéiques à partir d'images satellitaires et aériennes par processus Markov objet}, |
| year |
= |
{2003}, |
| journal |
= |
{Bulletin de la Société Française de Photogrammétrie et de Télédétection}, |
| volume |
= |
{170}, |
| pages |
= |
{13--22}, |
| url |
= |
{http://www.researchgate.net/profile/Nicolas_Baghdadi/publication/236882132_Extraction_automatique_des_rseaux_liniques__partir_dimages_satellitaires_et_ariennes_par_processus_Markov_objets/links/00463519e05ebd9e83000000.pdf?disableCoverPage=true}, |
| keyword |
= |
{} |
| } |
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5 Articles de conférence |
1 - Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework.
C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. European Signal Processing Conference (EUSIPCO), Antalya, Turkey, septembre 2005.
@INPROCEEDINGS{lacoste_eusipco05,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework}, |
| year |
= |
{2005}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Antalya, Turkey}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7078007}, |
| keyword |
= |
{} |
| } |
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2 - Unsupervised line network extraction from remotely sensed images by polyline process.
C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, septembre 2004.
@INPROCEEDINGS{lacoste04b,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Unsupervised line network extraction from remotely sensed images by polyline process}, |
| year |
= |
{2004}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{University of Technology, Vienna, Austria}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7079995}, |
| pdf |
= |
{http://www.eurasip.org/Proceedings/Eusipco/Eusipco2004/defevent/papers/cr1608.pdf}, |
| keyword |
= |
{} |
| } |
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3 - A Bayesian Geometric Model for Line Network Extraction from Satellite Images.
C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Quebec, Canada, mai 2004.
@INPROCEEDINGS{lacoste04a,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{A Bayesian Geometric Model for Line Network Extraction from Satellite Images}, |
| year |
= |
{2004}, |
| month |
= |
{mai}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Montreal, Quebec, Canada}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1326607}, |
| keyword |
= |
{} |
| } |
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4 - Extraction de réseaux linéiques à partir d'images satellitaires par processus Markov objet.
C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. GRETSI Symposium on Signal and Image Processing, Paris, France, septembre 2003.
@INPROCEEDINGS{lacosteXDJZNB03,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Extraction de réseaux linéiques à partir d'images satellitaires par processus Markov objet}, |
| year |
= |
{2003}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Paris, France}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/13529}, |
| keyword |
= |
{} |
| } |
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5 - Fusion of Radiometry and Textural Information for SIRC Image Classification.
O. Viveros-Cancino et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, septembre 2002.
@INPROCEEDINGS{oscarbaghdadi,
|
| author |
= |
{Viveros-Cancino, O. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Fusion of Radiometry and Textural Information for SIRC Image Classification}, |
| year |
= |
{2002}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Rochester, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1038916}, |
| keyword |
= |
{} |
| } |
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Rapport de recherche et Rapport technique |
1 - Hydrographic Network Extraction from Radar Satellite Imagesusing a Hierarchical Model within a Stochastic Geometry Framework.
C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Rapport de Recherche 5697, INRIA, France, septembre 2005.
@TECHREPORT{rrHimne,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Hydrographic Network Extraction from Radar Satellite Imagesusing a Hierarchical Model within a Stochastic Geometry Framework}, |
| year |
= |
{2005}, |
| month |
= |
{septembre}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5697}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00070318}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/07/03/18/PDF/RR-5697.pdf}, |
| keyword |
= |
{} |
| } |
Résumé :
Ce rapport présente un algorithme d'extraction non supervisée de réseaux hydrographiques à partir d'images satellitaires exploitant la structure arborescante de tels réseaux. L'extraction du surfacique (branches de largeur supérieure à trois pixels) est réalisée par un algorithme efficace fondé sur une modélisation par champ de Markov. Ensuite, l'extraction du linéique se fait par un algorithme récursif fondé sur un modèle hiérarchique dans lequel les affluents d'un fleuve donné sont modélisés par un processus ponctuel marqué défini dans le voisinage de ce fleuve. L'optimisation de chaque processus ponctuel est réalisée par un recuit simulé utilisant un algorithme de Monte Carlo par chaîne de Markov à sauts réversibles. Nous obtenons de bons résultats en terme d'omissions et de surdétections sur une image radar de type ERS. |
Abstract :
This report presents a two-step algorithm for unsupervised extraction of hydrographic networks from satellite images, that exploits the tree structures of such networks. First, the thick branches of the network are detected by an efficient algorithm based on a Markov random field. Second, the line branches are extracted using a recursive algorithm based on a hierarchical model of the hydrographic network, in which the tributaries of a given river are modeled by an object process (or a marked point process) defined within the neighborhood of this river. Optimization of each point process is done via simulated annealing using a reversible jump Markov chain Monte Carlo algorithm. We obtain encouraging results in terms of omissions and overdetections on a radar satellite image. |
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