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Publications about SAR Images
Result of the query in the list of publications :
4 Articles |
1 - SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model.
A. Achim and E.E. Kuruoglu and J. Zerubia. IEEE Trans. on Image Processing, 15(9): pages 2686-2693, September 2006.
Keywords : SAR Images.
@ARTICLE{jz_ieee_tr_ip_06,
|
| author |
= |
{Achim, A. and Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model}, |
| year |
= |
{2006}, |
| month |
= |
{September}, |
| journal |
= |
{IEEE Trans. on Image Processing}, |
| volume |
= |
{15}, |
| number |
= |
{9}, |
| pages |
= |
{2686-2693}, |
| pdf |
= |
{http://dx.doi.org/10.1109/TIP.2006.877362}, |
| keyword |
= |
{SAR Images} |
| } |
Abstract :
Synthetic aperture radar (SAR) images are inherently affected by a signal dependent noise known as speckle, which is due to the radar wave coherence. In this paper, we propose a novel adaptive despeckling filter and derive a maximum a posteriori (MAP) estimator for the radar cross section (RCS). We first employ a logarithmic transformation to change the multiplicative speckle into additive noise. We model the RCS using the recently introduced heavy-tailed Rayleigh density function, which was derived based on the assumption that the real and imaginary parts of the received complex signal are best described using the alpha-stable family of distribution. We estimate model parameters from noisy observations by means of second-kind statistics theory, which relies on the Mellin transform. Finally, we compare the proposed algorithm with several classical speckle filters applied on actual SAR images. Experimental results show that the homomorphic MAP filter based on the heavy-tailed Rayleigh prior for the RCS is among the best for speckle removal |
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2 - SAR amplitude probability density function estimation based on a generalized Gaussian model.
G. Moser and J. Zerubia and S.B. Serpico. IEEE Trans. on Image Processing, 15(6): pages 1429-1442, June 2006.
Keywords : SAR Images, Generalised Gaussians, Mellin transform.
Copyright : IEEE
@ARTICLE{moser_ieeeip05,
|
| author |
= |
{Moser, G. and Zerubia, J. and Serpico, S.B.}, |
| title |
= |
{SAR amplitude probability density function estimation based on a generalized Gaussian model}, |
| year |
= |
{2006}, |
| month |
= |
{June}, |
| journal |
= |
{IEEE Trans. on Image Processing}, |
| volume |
= |
{15}, |
| number |
= |
{6}, |
| pages |
= |
{1429-1442}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1632197}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00561372/en/}, |
| keyword |
= |
{SAR Images, Generalised Gaussians, Mellin transform} |
| } |
Abstract :
In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on synthetic aperture radar (SAR) data, this modeling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In this paper, an innovative parametric estimation methodology for SAR amplitude data is proposed that adopts a generalized Gaussian (GG) model for the complex SAR backscattered signal. A closed-form expression for the corresponding amplitude probability density function (PDF) is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model. Specifically, the recently proposed “method-of-log-cumulants” (MoLC) is applied, which stems from the adoption of the Mellin transform (instead of the usual Fourier transform) in the computation of characteristic functions and from the corresponding generalization of the concepts of moment and cumulant. For the developed GG-based amplitude model, the resulting MoLC estimates turn out to be numerically feasible and are also analytically proved to be consistent. The proposed parametric approach was validated by using several real ERS-1, XSAR, E-SAR, and NASA/JPL airborne SAR images, and the experimental results prove that the method models the amplitude PDF better than several previously proposed parametric models for backscattering phenomena. |
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3 - Dictionary-Based Stochastic Expectation-Maximization for SAR Amplitude Probability Density Function Estimation.
G. Moser and J. Zerubia and S.B. Serpico. IEEE Trans. Geoscience and Remote Sensing, 44(1): pages 188-200, January 2006.
Keywords : SAR Images, Stochastic EM (SEM), Dictionary.
Copyright : IEEE
@ARTICLE{moser_ieeetgrs_05,
|
| author |
= |
{Moser, G. and Zerubia, J. and Serpico, S.B.}, |
| title |
= |
{Dictionary-Based Stochastic Expectation-Maximization for SAR Amplitude Probability Density Function Estimation}, |
| year |
= |
{2006}, |
| month |
= |
{January}, |
| journal |
= |
{IEEE Trans. Geoscience and Remote Sensing}, |
| volume |
= |
{44}, |
| number |
= |
{1}, |
| pages |
= |
{188-200}, |
| url |
= |
{http://dx.doi.org/10.1109/TGRS.2005.859349}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00561369/en/}, |
| keyword |
= |
{SAR Images, Stochastic EM (SEM), Dictionary} |
| } |
Abstract :
In remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of the pixel intensities. This paper deals with the problem of probability density function (pdf) estimation in the context of synthetic aperture radar (SAR) amplitude data analysis. Several theoretical and heuristic models for the pdfs of SAR data have been proposed in the literature, which have been proved to be effective for different land-cover typologies, thus making the choice of a single optimal parametric pdf a hard task, especially when dealing with heterogeneous SAR data. In this paper, an innovative estimation algorithm is described, which faces such a problem by adopting a finite mixture model for the amplitude pdf, with mixture components belonging to a given dictionary of SAR-specific pdfs. The proposed method automatically integrates the procedures of selection of the optimal model for each component, of parameter estimation, and of optimization of the number of components by combining the stochastic expectation–maximization iterative methodology with the recently developed “method-of-log-cumulants” for parametric pdf estimation in the case of nonnegative random variables. Experimental results on several real SAR images are reported, showing that the proposed method accurately models the statistics of SAR amplitude data. |
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4 - Detecting codimension-two objects in an image with Ginzburg-Landau models.
G. Aubert and J.F. Aujol and L. Blanc-Féraud. International Journal of Computer Vision, 65(1-2): pages 29-42, November 2005.
Keywords : Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images.
@ARTICLE{laure-ijcv05,
|
| author |
= |
{Aubert, G. and Aujol, J.F. and Blanc-Féraud, L.}, |
| title |
= |
{Detecting codimension-two objects in an image with Ginzburg-Landau models}, |
| year |
= |
{2005}, |
| month |
= |
{November}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{65}, |
| number |
= |
{1-2}, |
| pages |
= |
{29-42}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/GL_IJCV_5.pdf}, |
| keyword |
= |
{Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images} |
| } |
Abstract :
In this paper, we propose a new mathematical model for detecting in an image singularities of codimension greater than or equal to two. This means we want to detect points in a 2-D image or points and curves in a 3-D image. We drew one's inspiration from
Ginzburg-Landau (G-L) models which have proved their efficiency for modeling many phenomena in physics. We introduce the model, state its
mathematical properties and give some experimental results demonstrating its capability in image processing. |
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2 Conference articles |
1 - Classification bayésienne supervisée d’images RSO de zones urbaines à très haute résolution.
A. Voisin and V. Krylov and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, September 2011.
Keywords : SAR Images, Classification, Urban areas, Markov Fields, Hierarchical models.
@INPROCEEDINGS{VoisinGretsi2011,
|
| author |
= |
{Voisin, A. and Krylov, V. and Zerubia, J.}, |
| title |
= |
{Classification bayésienne supervisée d’images RSO de zones urbaines à très haute résolution}, |
| year |
= |
{2011}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Bordeaux}, |
| url |
= |
{http://hal.inria.fr/inria-00623003/fr/}, |
| keyword |
= |
{SAR Images, Classification, Urban areas, Markov Fields, Hierarchical models} |
| } |
Résumé :
Ce papier présente un modèle de classification bayésienne supervisée d’images acquises par Radar à Synthèse d’Ouverture (RSO) très haute résolution en polarisation simple contenant des zones urbaines, particulièrement affectées par le bruit de chatoiement. Ce modèle prend en compte à la fois une représentation statistique des images RSO par modèle de mélanges finis et de copules, et une modélisation contextuelle
à partir de champs de Markov hiérarchiques. |
Abstract :
This paper deals with the Bayesian classification of single-polarized very high resolution synthetic aperture radar (SAR) images
that depict urban areas. The difficulty of such a classification relies in the significant effects of speckle noise. The model considered here takes into account both statistical modeling of images via finite mixture models and copulas, and contextual modeling thanks to hierarchical Markov random fields |
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2 - 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, 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}, |
| 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. |
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