Classification of SAR images using a general and tractable multiplicative model

Mejail, Marta; Jacobo-Berlles, Julio; Frery, Alejandro C.; Bustos, Oscar H.

doi:10.1080/0143116021000053274

Cited by 76 publications

(60 citation statements)

References 12 publications

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“…Therefore, the random weighting estimatorα Ã for α can be obtained via solving equation (12). By substituting the value ofα Ã into formula (9), we can obtain random weighting estimatorγ Ã .…”

Section: The Random Weighting Estimator Of Parametermentioning

confidence: 99%

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A robust estimator of parameters for G I 0 $$ {\mathcal{G}}_I^0 $$ -modeled SAR imagery based on random weighting method

Wang

Wen

2017

EURASIP J. Adv. Signal Process.

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In mono-polarized synthetic aperture radar (SAR) imagery, G 0 I distribution often is assumed as the universal model to characterize a large number of targets, which is indexed by three parameters: the number of looks, the scale parameter, and the roughness parameter. The latter is closely related to the number of elementary backscatters in each pixel, and it is the reason why so many researchers focus on it. Although many efforts have been paid on providing many estimates, numerical problems often exist in dependable estimation, such as 'outlier' and small samples and so on. Thus, a robust estimation scheme of two unknown parameters in G 0 I distribution based on random weighting method is proposed in this paper where the relationship between moments and parameters are utilized. Experimental results on SAR computational simulations data and real SAR images show that the particular scheme outperforms alternative forms of bias reduction mechanisms, and we can obtain more accurate estimation than that of other state-of-the-art algorithms.

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Section: The Random Weighting Estimator Of Parametermentioning

confidence: 99%

“…The backscatter (U) exhibits different degrees of homogeneity and can be modeled using the inverse gamma distribution, denoted by U~Γ − 1 (α, γ), whose probability density is [12], whose probability density is…”

Section: Introductionmentioning

confidence: 99%

A robust estimator of parameters for G I 0 $$ {\mathcal{G}}_I^0 $$ -modeled SAR imagery based on random weighting method

Wang

Wen

2017

EURASIP J. Adv. Signal Process.

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“…This distribution was proposed as a model for extremely heterogeneous areas [7], and Mejail et al [15,16] demonstrated it can be considered a universal model for speckled data. Data obeying the Γ law are referred to as "fully developed speckle", meaning that there is no texture in the wavelength of the illumination (which is in the order of centimeters).…”

Section: The Multiplicative Modelmentioning

confidence: 99%

Generalized Statistical Complexity of SAR Imagery

Almeida

Medeiros

Rosso

et al. 2012

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract.A new generalized Statistical Complexity Measure (SCM) was proposed by Rosso et al in 2010. It is a functional that captures the notions of order/disorder and of distance to an equilibrium distribution. The former is computed by a measure of entropy, while the latter depends on the definition of a stochastic divergence. When the scene is illuminated by coherent radiation, image data is corrupted by speckle noise, as is the case of ultrasound-B, sonar, laser and Synthetic Aperture Radar (SAR) sensors. In the amplitude and intensity formats, this noise is multiplicative and non-Gaussian requiring, thus, specialized techniques for image processing and understanding. One of the most successful family of models for describing these images is the Multiplicative Model which leads, among other probability distributions, to the G 0 law. This distribution has been validated in the literature as an expressive and tractable model, deserving the "universal" denomination for its ability to describe most types of targets. In order to compute the statistical complexity of a site in an image corrupted by speckle noise, we assume that the equilibrium distribution is that of fully developed speckle, namely the Gamma law in intensity format, which appears in areas with little or no texture. We use the Shannon entropy along with the Hellinger distance to measure the statistical complexity of intensity SAR images, and we show that it is an expressive feature capable of identifying many types of targets.

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“…"Sparse representation" refers specifically to an expression of the input signal as a linear combination of basic elements in which many of the coefficients are zero [10]. Recently, applications of sparse representation have achieved state-of-the-art performance.…”

Section: Sparse Representationmentioning

confidence: 99%

“…Thus, the high-level image processing and learning machine are useful for the classification of the complex scenes. Sparse representation of signals based on over-complete dictionary is a kind of new signal representation theory, which substitutes over-complete redundant function system for traditional orthogonal basis functions and provides great flexibility for adaptive sparse extension of signals [9][10][11][12][13]. It aims to approximate a target signal using a linear combination of elementary signals from a large candidate set, which is called as "dictionary", with each element called as "atom".…”

Section: Introductionmentioning

confidence: 99%

A Method of Polarimetric Synthetic Aperture Radar Image Classification Based on Sparse Representation

Wang

Xia

2016

TELKOMNIKA

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IntroductionFeature extraction and PolSAR image interpretation are of much theoretical and application significance, and recently, the extensive attention in PolSAR remote sensing has gradually arisen. PolSAR related researches have been conducted for many years, and a variety of methods have been proposed. Many of the proposed techniques are based on physical scattering mechanisms obtained from different polarimetric decomposition methods. Polarimetric target decomposition theorem expresses the average mechanism as the sum of independent elements in order to associate a physical mechanism with each resolution cell, which allows the identification and separation of scattering mechanisms in polarization signature for purposes of classification and recognition. Many descriptive parameters can be extracted by coherent and incoherent decomposition methods based on polarimetric characteristics. Freeman and Durden suggested a three-component decomposition method of polarimetric data, which introduced a combination of surface, double-bounce, and volume scatterings.Based on the idea of scattering model decomposition, a variety of incoherent decomposition models were proposed, such as four-component scattering model and multiplecomponent scattering model (MCSM) [1][2][3][4][5][6][7][8].The PolSAR image classification is actually a high-dimensional nonlinear mapping problem. The extensive analysis of the physical mechanism is difficult, and for complex scenes, the underlying physical mechanism of each pixel is hard to obtain. Thus, the high-level image processing and learning machine are useful for the classification of the complex scenes. Sparse representation of signals based on over-complete dictionary is a kind of new signal representation theory, which substitutes over-complete redundant function system for traditional orthogonal basis functions and provides great flexibility for adaptive sparse extension of signals [9][10][11][12][13]. It aims to approximate a target signal using a linear combination of elementary signals from a large candidate set, which is called as "dictionary", with each element called as "atom". Sparse representation has therefore increasingly become recognized as providing extremely high performance for diverse applications. The sparse representation-based target classification is a kind of new theory in SAR images processing, and has been proved to be rather efficacious [14].

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Classification of SAR images using a general and tractable multiplicative model

Cited by 76 publications

References 12 publications

A robust estimator of parameters for G I 0 $$ {\mathcal{G}}_I^0 $$ -modeled SAR imagery based on random weighting method

A robust estimator of parameters for G I 0 $$ {\mathcal{G}}_I^0 $$ -modeled SAR imagery based on random weighting method

Generalized Statistical Complexity of SAR Imagery

A Method of Polarimetric Synthetic Aperture Radar Image Classification Based on Sparse Representation

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