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The Publications
Result of the query in the list of publications :
31 PhD Thesis and Habilitations |
30 - Modélisation de la Redondance d'Image, Etude et Application à la Classification.
E. Volden. PhD Thesis, Ecole des Mines de Paris, January 1998.
@PHDTHESIS{volden,
|
| author |
= |
{Volden, E.}, |
| title |
= |
{Modélisation de la Redondance d'Image, Etude et Application à la Classification}, |
| year |
= |
{1998}, |
| month |
= |
{January}, |
| school |
= |
{Ecole des Mines de Paris}, |
| keyword |
= |
{} |
| } |
|
31 - Extraction de Thèmes Cartographiques dans les Images Satellites ou Aériennes. Application à la Génération de Quick-Looks Adaptatifs et à la Compression des Images.
J.M. Benharrosh. PhD Thesis, Universite de Nice Sophia Antipolis, 1998.
Note : papier (tu-0524)
@PHDTHESIS{benharrosh,
|
| author |
= |
{Benharrosh, J.M.}, |
| title |
= |
{Extraction de Thèmes Cartographiques dans les Images Satellites ou Aériennes. Application à la Génération de Quick-Looks Adaptatifs et à la Compression des Images}, |
| year |
= |
{1998}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| note |
= |
{papier (tu-0524)}, |
| keyword |
= |
{} |
| } |
|
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245 Conference articles |
1 - Change detection with synthetic aperture radar images by Wilcoxon statistic likelihood ratio test.
V. Krylov and G. Moser and A. Voisin and S.B. Serpico and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Orlando, United States, October 2012.
@INPROCEEDINGS{ICIP12,
|
| author |
= |
{Krylov, V. and Moser, G. and Voisin, A. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Change detection with synthetic aperture radar images by Wilcoxon statistic likelihood ratio test}, |
| year |
= |
{2012}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Orlando, United States}, |
| url |
= |
{http://hal.inria.fr/hal-00724284}, |
| keyword |
= |
{} |
| } |
|
2 - Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model.
A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In EURASIP, Bucarest, Romania, August 2012.
@INPROCEEDINGS{EURASIP12,
|
| author |
= |
{Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model}, |
| year |
= |
{2012}, |
| month |
= |
{August}, |
| booktitle |
= |
{EURASIP}, |
| address |
= |
{Bucarest, Romania}, |
| url |
= |
{http://hal.inria.fr/hal-00723286}, |
| keyword |
= |
{} |
| } |
|
3 - A Comparison of Texture and Amplitude based Unsupervised SAR Image Classifications for Urban Area Extraction.
K. Kayabol and J. Zerubia. In IEEE International Geoscience and Remote Sensing Symposium, pages 4054-4057, Munich, Germany, July 2012.
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4 - An hierarchical approach for model-based classification of SAR images.
K. Kayabol and J. Zerubia. In 20th Signal Processing and Communications Applications Conference, Mugla, Turkey, April 2012.
@INPROCEEDINGS{Kayabol12,
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| author |
= |
{Kayabol, K. and Zerubia, J.}, |
| title |
= |
{An hierarchical approach for model-based classification of SAR images}, |
| year |
= |
{2012}, |
| month |
= |
{April}, |
| booktitle |
= |
{20th Signal Processing and Communications Applications Conference}, |
| address |
= |
{Mugla, Turkey}, |
| url |
= |
{http://hal.inria.fr/hal-00686658}, |
| keyword |
= |
{} |
| } |
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5 - Multichannel hierarchical image classification using multivariate copulas.
A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In IS&T/SPIE Electronic Imaging – Computational Imaging X, San Francisco, United States, January 2012.
@INPROCEEDINGS{SPIE12,
|
| author |
= |
{Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Multichannel hierarchical image classification using multivariate copulas}, |
| year |
= |
{2012}, |
| month |
= |
{January}, |
| booktitle |
= |
{IS&T/SPIE Electronic Imaging – Computational Imaging X}, |
| address |
= |
{San Francisco, United States}, |
| url |
= |
{http://dx.doi.org/10.1117/12.917298}, |
| keyword |
= |
{} |
| } |
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6 - Building large urban environments from unstructured point data.
F. Lafarge and C. Mallet. In Proc. IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011.
@INPROCEEDINGS{lafarge_iccv11,
|
| author |
= |
{Lafarge, F. and Mallet, C.}, |
| title |
= |
{Building large urban environments from unstructured point data}, |
| year |
= |
{2011}, |
| month |
= |
{November}, |
| booktitle |
= |
{Proc. IEEE International Conference on Computer Vision (ICCV)}, |
| address |
= |
{Barcelona, Spain}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6126353}, |
| keyword |
= |
{} |
| } |
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7 - Synthetic Aperture Radar Image Classification via Mixture Approaches.
V. Krylov and J. Zerubia. In Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), Tel Aviv, Israel, November 2011.
Keywords : Synthetic Aperture Radar (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution.
Copyright : IEEE
@INPROCEEDINGS{krylovCOMCAS11,
|
| author |
= |
{Krylov, V. and Zerubia, J.}, |
| title |
= |
{Synthetic Aperture Radar Image Classification via Mixture Approaches}, |
| year |
= |
{2011}, |
| month |
= |
{November}, |
| booktitle |
= |
{Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS)}, |
| address |
= |
{Tel Aviv, Israel}, |
| url |
= |
{http://www.ortra.biz/comcas/}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00625551/en/}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution} |
| } |
Abstract :
In this paper we focus on the fundamental synthetic aperture radars (SAR) image processing problem of supervised classification. To address it we consider a statistical finite mixture approach to probability density function estimation. We develop a generalized approach to address the problem of mixture estimation and consider the use of several different classes of distributions as the base for mixture approaches. This allows performing the maximum likelihood classification which is then refined by Markov random field approach, and optimized by graph cuts. The developed method is experimentally validated on high resolution SAR imagery acquired by Cosmo-SkyMed and TerraSAR-X satellite sensors. |
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8 - Tree crown detection in high resolution optical images during the early growth stages of eucalyptus plantations in Brazil.
J. Zhou and C. Proisy and X. Descombes and J. Zerubia and G. Le Maire and Y. Nouvellon and P. Couteron. In Asian Conference on Pattern Recognition (ACPR), Beijing, China, November 2011.
Keywords : tree detection, Eucalyptus plantation, Marked point process, multi-date detection.
@INPROCEEDINGS{Zhou11,
|
| author |
= |
{Zhou, J. and Proisy, C. and Descombes, X. and Zerubia, J. and Le Maire, G. and Nouvellon, Y. and Couteron, P.}, |
| title |
= |
{Tree crown detection in high resolution optical images during the early growth stages of eucalyptus plantations in Brazil}, |
| year |
= |
{2011}, |
| month |
= |
{November}, |
| booktitle |
= |
{Asian Conference on Pattern Recognition (ACPR)}, |
| address |
= |
{Beijing, China}, |
| url |
= |
{http://hal.inria.fr/hal-00740973}, |
| keyword |
= |
{tree detection, Eucalyptus plantation, Marked point process, multi-date detection} |
| } |
Abstract :
Individual tree detection methods are more and more present, and improve, in forestry and silviculture domains with the increasing availability of satellite metric imagery. Automatic detection on these very high spatial resolution images aims to determine the tree positions and crown sizes. In this paper, we used a mathematical model based on marked point processes, which showed advantages w.r.t. several individual tree detection algorithms for plantations, to analyze the eucalyptus plantations in Brazil, with 2 optical images acquired by the WorldView-2 satellite. A tentative detection simultaneously with 2 images of different dates (multi-date) was tested for the first time, which estimates individual tree crown variation during these dates. The relevance of detection was discussed considering the detection performance in tree localizations and crown sizes. Then, tree crown growth was deduced from detection results and compared with the expected dynamics of corresponding populations. |
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