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Publications de Koray Kayabol
Résultat de la recherche dans la liste des publications :
Article |
1 - Unsupervised amplitude and texture classification of SAR images with multinomial latent model.
K. Kayabol et J. Zerubia. IEEE Trans. on Image Processing, 22(2): pages 561-572, février 2013.
Mots-clés : COSMOSkyMed, Classification EM, High resolution SAR, Jensen-Shannon criterion, Classification, Multinomial logistic.
@ARTICLE{KorayTIP2013,
|
| author |
= |
{Kayabol, K. and Zerubia, J.}, |
| title |
= |
{Unsupervised amplitude and texture classification of SAR images with multinomial latent model}, |
| year |
= |
{2013}, |
| month |
= |
{février}, |
| journal |
= |
{IEEE Trans. on Image Processing}, |
| volume |
= |
{22}, |
| number |
= |
{2}, |
| pages |
= |
{561-572}, |
| url |
= |
{http://hal.inria.fr/hal-00745387}, |
| keyword |
= |
{COSMOSkyMed, Classification EM, High resolution SAR, Jensen-Shannon criterion, Classification, Multinomial logistic} |
| } |
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3 Articles de conférence |
1 - A Comparison of Texture and Amplitude based Unsupervised SAR Image Classifications for Urban Area Extraction.
K. Kayabol et J. Zerubia. Dans IEEE International Geoscience and Remote Sensing Symposium, pages 4054-4057, Munich, Germany, juillet 2012.
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2 - An hierarchical approach for model-based classification of SAR images.
K. Kayabol et J. Zerubia. Dans 20th Signal Processing and Communications Applications Conference, Mugla, Turkey, avril 2012.
@INPROCEEDINGS{Kayabol12,
|
| author |
= |
{Kayabol, K. and Zerubia, J.}, |
| title |
= |
{An hierarchical approach for model-based classification of SAR images}, |
| year |
= |
{2012}, |
| month |
= |
{avril}, |
| booktitle |
= |
{20th Signal Processing and Communications Applications Conference}, |
| address |
= |
{Mugla, Turkey}, |
| url |
= |
{http://hal.inria.fr/hal-00686658}, |
| keyword |
= |
{} |
| } |
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3 - SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities.
K. Kayabol et A. Voisin et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), pages 173-176, Brussels, Belgium, septembre 2011.
Mots-clés : High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm.
@INPROCEEDINGS{inria-00592252,
|
| author |
= |
{Kayabol, K. and Voisin, A. and Zerubia, J.}, |
| title |
= |
{SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| pages |
= |
{173-176}, |
| address |
= |
{Brussels, Belgium}, |
| url |
= |
{http://hal.inria.fr/inria-00592252/en/}, |
| keyword |
= |
{High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm} |
| } |
Abstract :
We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes. To model the textures of the classes, we exploit a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error. Non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. We perform the classification Expectation-Maximization (CEM) algorithm to estimate the class parameters and classify the pixels. We obtained some classification results of water, land and urban areas in both supervised and semi-supervised cases on TerraSAR-X data. |
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Rapport de recherche et Rapport technique |
1 - Unsupervised amplitude and texture based classification of SAR images with multinomial latent model.
K. Kayabol et J. Zerubia. Rapport de Recherche 7700, INRIA, juillet 2011.
Mots-clés : High resolution SAR, Classification, Texture.
@TECHREPORT{Kayabol11,
|
| author |
= |
{Kayabol, K. and Zerubia, J.}, |
| title |
= |
{Unsupervised amplitude and texture based classification of SAR images with multinomial latent model}, |
| year |
= |
{2011}, |
| month |
= |
{juillet}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7700}, |
| url |
= |
{http://hal.archives-ouvertes.fr/hal-00612491/fr/}, |
| keyword |
= |
{High resolution SAR, Classification, Texture} |
| } |
Abstract :
We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes and a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error to model the textures of the classes. A non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. The Classification Expectation-Maximization (CEM) algorithm is performed to estimate the class parameters and to classify the pixels. We resort to Integrated Classification Likelihood (ICL) criterion to determine the number of classes in the model. We obtained some classification results of water, land and urban areas in both supervised and unsupervised cases on TerraSAR-X, as well as COSMO-SkyMed data.
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