Polarimetric sar speckle noise model

López-Martínez, Carlos; Fábregas, X.

doi:10.1109/tgrs.2003.815240

Cited by 155 publications

(100 citation statements)

References 25 publications

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“…Nevertheless, the characterization of speckle noise for results difficult as its different entries do not present the same mathematical structure. This drawback can be overcome by considering the equivalent matrix , since all its entries correspond to the Hermitian products of the components of (1) [9]. Since and are related by a unitary similarity transformation, they present the same eigenvalues but not the same eigenvectors.…”

Section: B Eigendecompositionmentioning

confidence: 99%

Statistical assessment of eigenvector-based target decomposition theorems in radar polarimetry

López-Martínez

Pottier

Cloude

2005

IEEE Trans. Geosci. Remote Sensing

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106

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Abstract-The performance of quantitative remote sensing based on multidimensional synthetic aperture radars (SARs), and polarimetric SAR systems in particular, depends strongly on a correct statistical characterization of the data, i.e., on a complete knowledge of the effects of the speckle noise. In this framework, the eigendecompostion of the covariance or coherency matrices and the associated decomposition have demonstrated the potential for quantitative estimation of physical parameters. In this paper, we present a detailed study of the statistics associated with this decomposition. This analysis requires the introduction of mathematical tools that are not well known in the remote sensing community. For this reason, we include a review section to present them. Using this work, we then present an expression for the probability density function of the sample eigenvalues of the covariance or coherency matrix. The availability of this expression allows a complete study of the separated sample eigenvalues, as well as, the entropy H and the anisotropy A. As demonstrated, all these parameters must be considered as asymptotically nonbiased with respect to the number of looks. In order to reduce the biases for a small number of averaged samples, a novel estimator for the eigenvalues is proposed. The results of this work are analyzed by means of simulated and real airborne SAR data. This analysis permits us to determine in detail the effects of the number of averaged samples in the estimation of physical information in radar polarimetry.

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Section: B Eigendecompositionmentioning

confidence: 99%

Statistical assessment of eigenvector-based target decomposition theorems in radar polarimetry

López-Martínez

Pottier

Cloude

2005

IEEE Trans. Geosci. Remote Sensing

Self Cite

106

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“…There are many options for the distributions of the mixing components, but here we mainly focus on the mixture of Wishart distributed components, since the Wishart distribution is widely used in PolSAR data modeling (Goodman 1963;Lee, Mitchell, and Kwok 1994;Tough, Blacknell, and Quegan 1995;López-Martínez and Fabregas 2003;Alonso-González, López-Martínez, and Salembier 2012). For different mixing components in the same image, the number of looks L is supposed to be consistent.…”

Section: Finite Mixture Modelmentioning

confidence: 99%

Log-cumulants of the finite mixture model and their application to statistical analysis of fully polarimetric UAVSAR data

Deng

Chen

et al. 2018

Geo-spatial Information Science

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Since its first flight in 2007, the UAVSAR instrument of NASA has acquired a large number of fully Polarimetric SAR (PolSAR) data in very high spatial resolution. It is possible to observe small spatial features in this type of data, offering the opportunity to explore structures in the images. In general, the structured scenes would present multimodal or spiky histograms. The finite mixture model has great advantages in modeling data with irregular histograms. In this paper, a type of important statistics called log-cumulants, which could be used to design parameter estimator or goodness-of-fit tests, are derived for the finite mixture model. They are compared with logcumulants of the texture models. The results are adopted to UAVSAR data analysis to determine which model is better for different land types. ARTICLE HISTORY

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“…Again, the statistical properties of the multilook data can be analyzed separately using (23), (29) and (30). The Wishart distribution is widely used in the modeling of PolSAR data [7,[36][37][38], and there are several variations that make the model more accurate or efficient.…”

Section: Wishart Distributionmentioning

confidence: 99%

Statistical Modeling of Polarimetric SAR Data: A Survey and Challenges

et al. 2017

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Knowledge of the exact statistical properties of the signal plays an important role in the applications of Polarimetric Synthetic Aperture Radar (PolSAR) data. In the last three decades, a considerable research effort has been devoted to finding accurate statistical models for PolSAR data, and a number of distributions have been proposed. In order to see the differences of various models and to make a comparison among them, a survey is provided in this paper. Texture models, which could capture the non-Gaussian behavior observed in high resolution data, and yet keep a compact mathematical form, are mainly explained. Probability density functions for the single look data and the multilook data are reviewed, as well as the advantages and applicable context of those models. As a summary, challenges in the area of statistical analysis of PolSAR data are also discussed.

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Polarimetric sar speckle noise model

Cited by 155 publications

References 25 publications

Statistical assessment of eigenvector-based target decomposition theorems in radar polarimetry

Statistical assessment of eigenvector-based target decomposition theorems in radar polarimetry

Log-cumulants of the finite mixture model and their application to statistical analysis of fully polarimetric UAVSAR data

Statistical Modeling of Polarimetric SAR Data: A Survey and Challenges

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