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

Abstract

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.

Application of bootstrap techniques for the estimation of Target Decomposition parameters in RADAR polarimetry

“…2. We observed that the bias reduction from the AQ-MLE [2] and the jackknife are similar (a slightly better bias reduction was obtained for the third eigenvalue with the jackknife). …”

“…Previous approaches for bias-correction in sample eigenvalues include an asymptotic analysis by Lopèz-Martinez et al [2] based on a derivation of the sample eigenvalue probability density function (pdf). The resulting estimator (AQ-MLE) has been shown to significantly reduce the bias for the Anisotropy and the Entropy but not for alpha.…”

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

“…Cloude and Pottier calculated an entropy H and an angle α and then linearly separated the H-α plane into nine zones within a feasible region to determine the basic scattering mechanisms. In recent years, Cloude-Pottier decomposition has been analyzed, improved, and widely applied in segmentation, classification, and detection applications [17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

G0-Wishart Distribution Based Classification From Polarimetric Sar Images

“…Using NASA/JPL AIRSAR and UAVSAR, L-Band, multi-look fully polarimetric complex images. First of all, to reduce speckle influence for classification result and preserve image resolution, while the original PolSAR image does not have a large enough Equivalent Number of Looks(ENL), we choose multilook polarimetric SAR data process by averaging several independent 1-look coherent matrices and speckle filter with a small window (Lopez-Martinez, 2005). Apply zones in the H/A/ plane to initially classify the image into sixteen classes.…”