Estimation of sea-ice SAR clutter statistics from Pearson's system of distributions

Derrode, Stéphane; Mercier, Grégoire; Caillec, J.-M. Le; Garello, R.

doi:10.1109/igarss.2001.976098

Cited by 16 publications

(9 citation statements)

References 8 publications

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“…c. Stop the procedure when the estimates become steady, according to some criterion specific to a given application dealt with. Let us remark that the use of the Pearson system has already been used in different hidden Markov models and gave satisfactory results [6], [8], [9], [10]. Therefore, the originality of the present paper lies in the fact that we take spatial correlations into account, which generalize the previous models.…”

Section: Parameter Estimationmentioning

confidence: 79%

Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields

Benboudjema

Pieczynski

2007

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Recent developments in statistical theory and associated computational techniques have opened new avenues for image modeling as well as for image segmentation techniques. Thus, a host of models have been proposed and the ones which have probably received considerable attention are the hidden Markov fields (HMF) models. This is due to their simplicity of handling and their potential for providing improved image quality. Although these models provide satisfying results in the stationary case, they can fail in the nonstationary one. In this paper, we tackle the problem of modeling a nonstationary hidden random field and its effect on the unsupervised statistical image segmentation. We propose an original approach, based on the recent triplet Markov field (TMF) model, which enables one to deal with nonstationary class fields. Moreover, the noise can be correlated and possibly non-Gaussian. An original parameter estimation method which uses the Pearson system to find the natures of the noise margins, which can vary with the class, is also proposed and used to perform unsupervised segmentation of such images. Experiments indicate that the new model and related processing algorithm can improve the results obtained with the classical ones.

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Section: Parameter Estimationmentioning

confidence: 79%

Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields

Benboudjema

Pieczynski

2007

IEEE Trans. Pattern Anal. Mach. Intell.

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“…This system consists of mainly eight families of distributions of various types with mono-modal and possibly non symmetrical shapes (Gamma, Exponential and Beta distributions among others) and has shown to be efficient in HMC in the context of radar image segmentation 5,24 . Let µ 2 , µ 3 and µ 4 denote the centered moments of order 2, 3 and 4.…”

Section: Ice In Pearson Pmcmentioning

confidence: 99%

<title>SAR image segmentation using generalized pairwise Markov chains</title>

Derrode

Pieczynski²

2003

SPIE Proceedings

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The efficiency of Markov models in the context of SAR image segmentation mainly relies on their spatial regularity constraint. However, a pixel may have a rather different visual aspect when it is located near a boundary or inside a large set of pixels of the same class. According to the classical hypothesis in Hidden Markov Chain (HMC) models, this fact can not be taken into consideration. This is the very reason of the recent Pairwise Markov Chains (PMC) model which relies on the hypothesis that the pairwise process (X, Y ) is Markovian and stationary, but not necessarily X. The main interest of the PMC model in SAR image segmentation is to not assume that the speckle is spatially uncorrelated. Hence, it is possible to take into account the difference between two successive pixels that belong to the same region or that overlap a boundary. Both PMC and HMC parameters are learnt from a variant of the Iterative Conditional Estimation method. This allows to apply the Bayesian Maximum Posterior Marginal criterion for the restoration of X in an unsupervised manner. We will compare the PMC model with respect to the HMC one for the unsupervised segmentation of SAR images, for both Gaussian distributions and Pearson system of distributions.

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“…The traditional popular tool used for texture-based segmentation and classification is the Gray Level Cooccurrence Matrix (GLCM) [4,5], but some other techniques have been proposed with success : description through wavelet coefficient statistics [6], the Markov Random Field [7] or Markov Chain [8] models.…”

Section: Introductionmentioning

confidence: 99%

Generalized gray level dependence method for prostate cancer classification

Khelifi

Adel

Bourennane

et al. 2011

International Workshop on Systems, Signal Processing and Their Applications, WOSSPA

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In this paper, we present a new approach for multi-spectral texture classification. Therefore, we aim to add spectral information to classical texture analysis methods that only treat gray-level spatial variations. To achieve this goal, we propose a Spatial and Spectral Gray Level Dependence Method (SSGLDM) in order to extend the concept of spatial gray level dependence method by assuming texture joint information between spectral bands. In addition, the new texture features measurement related to (SSGLDM) which define the image properties have been also proposed. Extensive experiments have been carried out on many multispectral images for use in prostate cancer diagnosis and quantitative results showed the efficiency of this method compared to the Gray Level Co-occurrence Matrix (GLCM). The results indicate a significant improvement in classification accuracy.

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Estimation of sea-ice SAR clutter statistics from Pearson's system of distributions

Cited by 16 publications

References 8 publications

Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields

Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields

<title>SAR image segmentation using generalized pairwise Markov chains</title>

Generalized gray level dependence method for prostate cancer classification

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