Feature motivated polarization scattering matrix decomposition

Cameron, William L.; Leung, L.K.

doi:10.1109/radar.1990.201088

Cited by 164 publications

(97 citation statements)

References 2 publications

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“…The interest on the exterior product of independent differential forms lies in the fact that the Jacobian's determinant of a given transformation can be derived as the exterior product of differential forms. Given a transformation of coordinates , where , for , refer to the coordinates of the domain of and , for , denote the coordinates of the range, then (17) In (17), the term can be clearly identified as the Jacobian's determinant of the corresponding transformation . Given a matrix , the exterior product of the independent differential components is denoted by , called the volume element.…”

Section: Sample Eigenvalues Pdf: a Reviewmentioning

confidence: 99%

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

López-Martínez

Pottier

Cloude

2005

IEEE Trans. Geosci. Remote Sensing

107

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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: Sample Eigenvalues Pdf: a Reviewmentioning

confidence: 99%

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

López-Martínez

Pottier

Cloude

2005

IEEE Trans. Geosci. Remote Sensing

107

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“…Inserting the ML estimates [65] in (31), the decision rule for classification is (32) Polarimetric classification of scattering matrices was previously suggested in [71] and [72]; however, (31) and (32) implement the concept in a GLRT by computing ML estimates of amplitude, phase, and orientation angle, and incorporating a SIRV clutter covariance model. The prescreener in (28) is an extension of the binary GLRT in [63] and [73] that classifies each pixel as either dihedral in clutter or as clutter.…”

Section: B Glrt Processingmentioning

confidence: 99%

Attributed scattering centers for SAR ATR

Potter

Moses

1997

IEEE Trans. on Image Process.

440

210

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Abstract-High-frequency radar measurements of man-made targets are dominated by returns from isolated scattering centers, such as corners and flat plates. Characterizing the features of these scattering centers provides a parsimonious, physically relevant signal representation for use in automatic target recognition (ATR). In this paper, we present a framework for feature extraction predicated on parametric models for the radar returns. The models are motivated by the scattering behavior predicted by the geometrical theory of diffraction. For each scattering center, statistically robust estimation of model parameters provides highresolution attributes including location, geometry, and polarization response. We present statistical analysis of the scattering model to describe feature uncertainty, and we provide a leastsquares algorithm for feature estimation. We survey existing algorithms for simplified models, and derive bounds for the error incurred in adopting the simplified models. A model order selection algorithm is given, and an M-ary generalized likelihood ratio test is given for classifying polarimetric responses in spherically invariant random clutter.

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“…This approach represents target scattering by several basic scattering mechanisms. Since 1970, this technique has become an advanced research area in polarimetric SAR signal processing, with many valuable coherent and incoherent decompositions being developed [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Among these methods, Cloude-Pottier decomposition has attracted considerable attention.…”

Section: Introductionmentioning

confidence: 99%

Scattering Mechanism Extraction by a Modified Cloude-Pottier Decomposition for Dual Polarization SAR

Wu²

2015

Remote Sensing

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Dual polarization is a typical operational mode of polarimetric synthetic aperture radar (SAR). However, few studies have considered the scattering mechanism extraction of dual-polarization SARs. A modified Cloude-Pottier decomposition is proposed to investigate the performance of the scattering mechanism extraction of dual-polarization SARs. It is theoretically demonstrated that only HH-VV SAR can discriminate the three canonical scattering mechanisms from an isotropic surface, horizontal dipole, and isotropic dihedral. Various experiments are conducted using 21 scenes from real datasets acquired by AIRSAR, Convair-580 SAR, EMISAR, E-SAR, Pi-SAR, and RADARSAT-2. Division of the dual-polarization H-α plane is experimentally obtained. The lack of cross-polarization induces the diffusion of scattering mechanisms and their overlap in the HH-VV H-α plane. However, the performance of HH-VV SAR for extracting scattering mechanisms is acceptable. Thus, HH-VV SAR is a suitable alternative to full-polarization SAR in certain cases. Meanwhile, the extraction performance of the other two dual-polarization SARs is badly degraded due to the lack of co-polarization. Therefore, HH-HV and HV-VV SARs cannot effectively extract the scattering mechanisms in the H-α plane.

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Feature motivated polarization scattering matrix decomposition

Cited by 164 publications

References 2 publications

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

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

Attributed scattering centers for SAR ATR

Scattering Mechanism Extraction by a Modified Cloude-Pottier Decomposition for Dual Polarization SAR

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