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Publications sur High resolution SAR
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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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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