SAR image classification based on alpha-stable distribution

Yan, Peng; Xu, Xin; Zhou, Wanbin; Zhao, Yin

doi:10.1080/01431161.2010.487878

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…terms is Alpha-stable. Moreover, Alpha-stable distribution has been used as a successful alternative for modeling non-Gaussian data and has also been applied to understand SAR images, e.g., for image restoration [17], object detection [20,21], image classification [22], and image fusion [23]. Applications of Alpha-stable distribution in other areas such as watermark detection, network traffic and stock returns have also been reported [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

SAR Images Statistical Modeling and Classification Based on the Mixture of Alpha-Stable Distributions

Yan

Chen

et al. 2013

Remote Sensing

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Abstract:This paper proposes the mixture of Alpha-stable (MAS) distributions for modeling statistical property of Synthetic Aperture Radar (SAR) images in a supervised Markovian classification algorithm. Our work is motivated by the fact that natural scenes consist of various reflectors with different types that are typically concentrated within a small area, and SAR images generally exhibit sharp peaks, heavy tails, and even multimodal statistical property, especially at high resolution. Unimodal distributions do not fit such statistical property well, and thus a multimodal approach is necessary. Driven by the multimodality and impulsiveness of high resolution SAR images histogram, we utilize the mixture of Alpha-stable distributions to describe such characteristics. A pseudo-simulated annealing (PSA) estimator based on Markov chain Monte Carlo (MCMC) is present to efficiently estimate model parameters of the mixture of Alpha-stable distributions. To validate the proposed PSA estimator, we apply it to simulated data and compare its performance to that of a state-of-the-art estimator. Finally, we exploit the MAS distributions and a Markovian context for SAR images classification. The effectiveness of the proposed classifier is demonstrated by experiments on TerraSAR-X images, which verifies the validity of the MAS distributions for modeling and classification of SAR images.

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Section: Introductionmentioning

confidence: 99%

SAR Images Statistical Modeling and Classification Based on the Mixture of Alpha-Stable Distributions

Yan

Chen

et al. 2013

Remote Sensing

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“…Lévy [47] pioneered the α-stable distribution, which has been widely used in financial time series [25,49,68] and, recently, applied in SAR image processing [59,60,69]. This law has not tractable pdf expression and is often represented by its characteristic function (cf) given by: For −∞ < t < ∞,…”

Section: The α-Stable Heavy-tailed Rayleigh and Cauchy-rayleigh Modelsmentioning

confidence: 99%

Closed-form mathematical expressions for the exponentiated Cauchy-Rayleigh distribution

VedoVatto,

Nascimento

2017

Preprint

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The Cauchy-Rayleigh (CR) distribution has been successfully used to describe asymmetric and heavy-tail events from radar imagery. Employing such model to describe lifetime data may then seem attractive, but some drawbacks arise: its probability density function does not cover non-modal behavior as well as the CR hazard rate function (hrf) assumes only one form. To outperform this difficulty, we introduce an extended CR model, called exponentiated Cauchy-Rayleigh (ECR) distribution. This model has two parameters and hrf with decreasing, decreasing-increasing-decreasing and upside-down bathtub forms. In this paper, several closed-form mathematical expressions for the ECR model are proposed: median, mode, probability weighted, log-, incomplete and order statistic moments and Fisher information matrix. We propose three estimation procedures for the ECR parameters: maximum likelihood (ML), bias corrected ML and percentile-based methods. A simulation study is done to assess the performance of estimators. An application to survival time of heart problem patients illustrates the usefulness of the ECR model. Results point out that the ECR distribution may outperform classical lifetime models, such as the gamma, Birnbaun-Saunders, Weibull and log-normal laws, before heavy-tail data.

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“…They have strong theoretical foundations and excellent empirical successes. They have been applied to different applications such as SAR classifications [13], Face Recognition [14]...…”

Section: B Support Vector Machinementioning

confidence: 99%

Sonar image segmentation based on statistical modeling of wavelet subbands

Karine

Lasmar

Baussard

2015

2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA)

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This paper deals with the classification and segmentation of seafloor images recorded by sidescan sonar. To address this problem, related to texture analysis, a supervised approach is considered. The features of the textured images are extract by characterizing the wavelet coefficients through parametric probabilistic models. In this contribution, the generalized Gaussian distribution and the α-stable distribution are used. For the classification step, two classifiers are considered: the k-nearest neighbor algorithm, that exploit the Kullback-Leibler divergence as similarity measurement, and the support vector machines. Experimental results on sonar images demonstrate the effectiveness of the proposed approach for sonar image classification and segmentation.

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SAR image classification based on alpha-stable distribution

Cited by 9 publications

References 15 publications

SAR Images Statistical Modeling and Classification Based on the Mixture of Alpha-Stable Distributions

SAR Images Statistical Modeling and Classification Based on the Mixture of Alpha-Stable Distributions

Closed-form mathematical expressions for the exponentiated Cauchy-Rayleigh distribution

Sonar image segmentation based on statistical modeling of wavelet subbands

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