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Publications sur Textural features
Résultat de la recherche dans la liste des publications :
Article |
1 - Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model.
A. Voisin et V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. IEEE Geoscience and Remote Sensing Letters, 2012.
Note : to appear in 2013
Mots-clés : 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} |
| } |
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Article de conférence |
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 et G. Moser et V. Krylov et S.B. Serpico et J. Zerubia. Dans Proc. of SPIE (SPIE Symposium on Remote Sensing 2010), Vol. 7830, Toulouse, France, septembre 2010.
Mots-clés : Images SAR, Supervised classification, Zones urbaines, 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 |
= |
{septembre}, |
| booktitle |
= |
{Proc. of SPIE (SPIE Symposium on Remote Sensing 2010)}, |
| volume |
= |
{7830}, |
| 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 |
= |
{Images SAR, Supervised classification, Zones urbaines, 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. |
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