Parametric and nonparametric tests for speckled imagery

Cintra, Renato J.; Frery, Alejandro C.; Nascimento, Abraão D. C.

doi:10.1007/s10044-011-0249-3

Cited by 16 publications

(24 citation statements)

References 42 publications

(73 reference statements)

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“…Nascimento et al [29] used the same approach for intensity data under the G 0 I distribution, and compared the performance of test statistics derived from a number of (h-φ) distances (Kullback-Leibler, Rényi, Hellinger, Bhattacharyya, Jensen-Shannon, Arithmetic-Geometric, Triangular, and Harmonic Mean) as features for image classification. Cintra et al [35] compared some of these measures with parametric and nonparametric tests, and they outperformed other techniques in terms of efficiency and robustness.…”

Section: Stochastic Distancesmentioning

confidence: 99%

Modifying the Yamaguchi Four-Component Decomposition Scattering Powers Using a Stochastic Distance

Bhattacharya

Muhuri

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-Model-based decompositions have gained considerable attention after the initial work of Freeman and Durden. This decomposition which assumes the target to be reflection symmetric was later relaxed in the Yamaguchi et al. decomposition with the addition of the helix parameter. Since then many decomposition have been proposed where either the scattering model was modified to fit the data or the coherency matrix representing the second order statistics of the full polarimetric data is rotated to fit the scattering model. In this paper we propose to modify the Yamaguchi four-component decomposition (Y4O) scattering powers using the concept of statistical information theory for matrices. In order to achieve this modification we propose a method to estimate the polarization orientation angle (OA) from full-polarimetric SAR images using the Hellinger distance. In this method, the OA is estimated by maximizing the Hellinger distance between the un-rotated and the rotated T33 and the T22 components of the coherency matrix [T]. Then, the powers of the Yamaguchi four-component model-based decomposition (Y4O) are modified using the maximum relative stochastic distance between the T33 and the T22 components of the coherency matrix at the estimated OA. The results show that the overall double-bounce powers over rotated urban areas have significantly improved with the reduction of volume powers. The percentage of pixels with negative powers have also decreased from the Y4O decomposition. The proposed method is both qualitatively and quantitatively compared with the results obtained from the Y4O and the Y4R decompositions for a Radarsat-2 C-band SanFrancisco dataset and an UAVSAR L-band Hayward dataset.

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Section: Stochastic Distancesmentioning

confidence: 99%

Modifying the Yamaguchi Four-Component Decomposition Scattering Powers Using a Stochastic Distance

Bhattacharya

Muhuri

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The covariance matrices for each class were the estimated covariance matrices, using the training samples presented in Figure 1(a), whose numbers of pixels are shown in Table III. These covariance matrices are presented in equations (14)- (22) of the appendix. The simulation was performed with four looks and three polarization bands, HH, HV and VV.…”

Section: B Simulated Data Descriptionmentioning

confidence: 99%

“…The covariance matrices of the nine classes of the SIR-C images were estimated by maximum likelihood using the selected training samples (Figure 1(a) and Table III). Equations (14) to (22) present the estimated covariance matrices for the following classes: River, Caatinga, Prepared Soil, Soybean 1, Soybean 2, Soybean 3, Tillage, Corn 1, and Corn 2, respectively. These matrices were the parameter used for image simulation under the Wishart model, as described in section V-B.…”

Section: Appendixmentioning

confidence: 99%

Classification of Segments in PolSAR Imagery by Minimum Stochastic Distances Between Wishart Distributions

Silva

Freitas²,

Sant’Anna³

et al. 2013

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A new classifier for Polarimetric SAR (PolSAR) images is proposed and assessed in this paper. Its input consists of segments, and each one is assigned the class which minimizes a stochastic distance. Assuming the complex Wishart model, several stochastic distances are obtained from the h-φ family of divergences, and they are employed to derive hypothesis test statistics that are also used in the classification process. This article also presents, as a novelty, analytic expressions for the test statistics based on the following stochastic distances between complex Wishart models: Kullback-Leibler, Bhattacharyya, Hellinger, Rényi, and Chi-Square; also, the test statistic based on the Bhattacharyya distance between multivariate Gaussian distributions is presented. The classifier performance is evaluated using simulated and real PolSAR data. The simulated data are based on the complex Wishart model, aiming at the analysis of the proposal well controlled data. The real data refer to the complex L-band image, acquired during the 1994 SIR-C mission. The results of the proposed classifier are compared with those obtained by a Wishart per-pixel/contextual classifier, and we show the better performance of the region-based classification. The influence of the statistical modeling is assessed by comparing the results using the Bhattacharyya distance between multivariate Gaussian distributions for amplitude data. The results with simulated data indicate that the proposed classification method has a very good performance when the data follow the Wishart model. The proposed classifier also performs better than the per-pixel/contextual classifier and the Bhattacharyya Gaussian distance using SIR-C PolSAR data.

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“…Cintra et al [19] showed that these statistics outperform the Kolmogorov-Smirnov nonparametric test in intensity SAR imagery. In this paper, we aim to quantify contrast in PolSAR images and situations with r = 2.…”

Section: B Asymptotic Variancesmentioning

confidence: 99%

“…A comprehensive examination of these measures is presented and applied to intensity SAR data in [18], [19], and to PolSAR models in [20].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Statistical Analysis of PolSAR Data

Frery

Cintra

Nascimento

2013

IEEE Trans. Geosci. Remote Sensing

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Abstract-Images obtained from coherent illumination processes are contaminated with speckle noise, with polarimetric synthetic aperture radar (PolSAR) imagery as a prominent example. With an adequacy widely attested in the literature, the scaled complex Wishart distribution is an acceptable model for PolSAR data. In this perspective, we derive analytic expressions for the Shannon, Rényi, and restricted Tsallis entropies under this model. Relationships between the derived measures and the parameters of the scaled Wishart law (i.e., the equivalent number of looks and the covariance matrix) are discussed. In addition, we obtain the asymptotic variances of the Shannon and Rényi entropies when replacing distribution parameters by maximum likelihood estimators. As a consequence, confidence intervals based on these two entropies are also derived and proposed as new ways of capturing contrast. New hypothesis tests are additionally proposed using these results, and their performance is assessed using simulated and real data. In general terms, the test based on the Shannon entropy outperforms those based on Rényi's.

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Parametric and nonparametric tests for speckled imagery

Cited by 16 publications

References 42 publications

Modifying the Yamaguchi Four-Component Decomposition Scattering Powers Using a Stochastic Distance

Modifying the Yamaguchi Four-Component Decomposition Scattering Powers Using a Stochastic Distance

Classification of Segments in PolSAR Imagery by Minimum Stochastic Distances Between Wishart Distributions

Entropy-Based Statistical Analysis of PolSAR Data

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