|
|
Publications about Supervised classification
Result of the query in the list of publications :
2 Articles |
1 - Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model.
A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. IEEE Geoscience and Remote Sensing Letters, 2012.
Note : to appear in 2013
Keywords : Hierarchical Markov random fields (MRFs) , Supervised classification, synthetic aperture radar (SAR), Textural features, urban areas, wavelets.
@ARTICLE{Voisin13,
|
| author |
= |
{Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model}, |
| year |
= |
{2012}, |
| journal |
= |
{IEEE Geoscience and Remote Sensing Letters}, |
| note |
= |
{to appear in 2013}, |
| url |
= |
{http://dx.doi.org/10.1109/LGRS.2012.2193869}, |
| keyword |
= |
{Hierarchical Markov random fields (MRFs) , Supervised classification, synthetic aperture radar (SAR), Textural features, urban areas, wavelets} |
| } |
|
2 - Supervised High Resolution Dual Polarization SAR Image Classification by Finite Mixtures and Copulas.
V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. IEEE Journal of Selected Topics in Signal Processing, 5(3): pages 554-566, June 2011.
Keywords : Polarimetric synthetic aperture radar, Supervised classification, probability density function (pdf), dictionary-based pdf estimation, Markov random field, copula.
Copyright : IEEE
@ARTICLE{krylovJSTSP2011,
|
| author |
= |
{Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Supervised High Resolution Dual Polarization SAR Image Classification by Finite Mixtures and Copulas}, |
| year |
= |
{2011}, |
| month |
= |
{June}, |
| journal |
= |
{ IEEE Journal of Selected Topics in Signal Processing}, |
| volume |
= |
{5}, |
| number |
= |
{3}, |
| pages |
= |
{554-566}, |
| url |
= |
{http://dx.doi.org/10.1109/JSTSP.2010.2103925}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00562326/en/}, |
| keyword |
= |
{Polarimetric synthetic aperture radar, Supervised classification, probability density function (pdf), dictionary-based pdf estimation, Markov random field, copula} |
| } |
Abstract :
In this paper a novel supervised classification approach is proposed for high resolution dual polarization (dualpol) amplitude satellite synthetic aperture radar (SAR) images. A novel probability density function (pdf) model of the dual-pol SAR data is developed that combines finite mixture modeling for marginal probability density functions estimation and copulas for multivariate distribution modeling. The finite mixture modeling is performed via a recently proposed SAR-specific dictionarybased stochastic expectation maximization approach to SAR amplitude pdf estimation. For modeling the joint distribution of dual-pol data the statistical concept of copulas is employed, and a novel copula-selection dictionary-based method is proposed. In order to take into account the contextual information, the developed joint pdf model is combined with a Markov random field approach for Bayesian image classification. The accuracy of the developed dual-pol supervised classification approach is validated and compared with benchmark approaches on two high resolution dual-pol TerraSAR-X scenes, acquired during an epidemiological study. A corresponding single-channel version of the classification algorithm is also developed and validated on a single polarization COSMO-SkyMed scene. |
|
 top of the page
Conference article |
1 - Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features.
A. Voisin and G. Moser and V. Krylov and S.B. Serpico and J. Zerubia. In Proc. of SPIE (SPIE Symposium on Remote Sensing 2010), Vol. 7830, pages 78300O, Toulouse, France, September 2010.
Keywords : SAR Images, Supervised classification, Urban areas, Textural features, Copulas, Markov Random Fields.
Copyright : SPIE
@INPROCEEDINGS{7830-23,
|
| author |
= |
{Voisin, A. and Moser, G. and Krylov, V. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. of SPIE (SPIE Symposium on Remote Sensing 2010)}, |
| volume |
= |
{7830}, |
| pages |
= |
{78300O}, |
| address |
= |
{Toulouse, France}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00516333/en}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/51/63/33/PDF/Classification_of_VHR_SAR_SPIE_sept2010_Toulouse_Voisin.pdf}, |
| keyword |
= |
{SAR Images, Supervised classification, Urban areas, Textural features, Copulas, Markov Random Fields} |
| } |
Abstract :
This paper addresses the problem of the classification of very high resolution SAR amplitude images of urban areas. The proposed supervised method combines a finite mixture technique to estimate class-conditional probability density functions, Bayesian classification, and Markov random fields (MRFs). Textural features, such as those extracted by the grey-level co-occurrency method, are also integrated in the technique, as they allow improving the discrimination of urban areas. Copula theory is applied to estimate bivariate joint class-conditional statistics, merging the marginal distributions of both textural and SAR amplitude features. The resulting joint distribution estimates are plugged into a hidden MRF model, endowed with a modified Metropolis dynamics scheme for energy minimization. Experimental results with COSMO-SkyMed images point out the accuracy of the proposed method, also as compared with previous contextual classifiers. |
|
top of the page
Technical and Research Report |
1 - Classification of very high resolution SAR images of urban areas.
A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. Rapport de recherche 7758, INRIA, October 2011.
Keywords : Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet.
@TECHREPORT{RR-7758,
|
| author |
= |
{Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Classification of very high resolution SAR images of urban areas}, |
| year |
= |
{2011}, |
| month |
= |
{October}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Rapport de recherche}, |
| number |
= |
{7758}, |
| url |
= |
{http://hal.inria.fr/docs/00/63/10/38/PDF/RR-7758.pdf}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet} |
| } |
Résumé :
Dans le cadre d’une approche face aux risques environnementaux, nous proposons une nouvelle méthode de classification bayésienne supervisée. Celle-ci combine une modélisation statistique des images avec une prise en compte contextuelle via des champs de Markov hiérarchiques. Ce rapport de recherche vise à détailler plus amplement cette modélisation contextuelle, à savoir expliciter le modèle mathématique sur quad-arbre et l’obtention des observations par décomposition en ondelettes de l’image originale. Il met également en exergue certaines modifications apportées en
vue d’améliorer la classification finale. |
Abstract :
In the framework of the assessment of environmental risks, we propose herein a new supervised Bayesian classification method. It combines statistical image modeling with a contextual approach via hierarchical Markov random fields. This research report aims to further focus on this kind of contextual classification approach by detailing both the quad-tree mathematical model and the statistics of the observations, obtained by wavelet transform. We therefore introduce modifications to a classical Markovian single-scale algorithm that lead to more accurate classification results. |
|
top of the page
These pages were generated by 
|
get the bibtex
