Search

In this article, we propose an efficient heterogeneous change detection algorithm based on stochastic distance measure between two $\mathcal {G}_{d}^{0}$ distributions. Due to its flexibility and simplicity, the matrix-variate $\mathcal {G}_{d}^{0}$ distribution has been successfully used to model the multilook polarimetric synthetic aperture radar (PolSAR) data and has been tested for classification, segmentation, and image analysis. Concretely, closed-form expressions for the Kullback–Leibler, Renyi of order $\beta $ , Bhattacharyya, and Hellinger distances are provided to compute the stochastic distance between $\mathcal {G}_{d}^{0}$ distributions. In this context, we resort to the expectation–maximization (EM) to estimate accurately with low complexity for the parameters of the probability distribution of the two multilook polarimetric covariance matrices to be compared. Finally, the performance of the method is compared firstly to the performance of other known distributions, such as the scaled complex Wishart distribution, and secondly to other known statistical tests using simulated and real multilook PolSAR data. [ABSTRACT FROM AUTHOR]

This letter presents a novel method to estimate the equivalent number of looks (ENLs) and the texture parameters when multilook polarimetric SAR data are stochastically modeled by the product model. The method is based on the fractional moments of the determinant of the multilook polarimetric covariance (FMDC) matrix. The estimator of ENL is independent of the distribution of the texture model, and the estimator of the texture parameters can be derived for all commonly used multilook PolSAR distributions. $\mathcal {K}_{d}$ is given in this letter as multilook PolSAR distribution. The performance of the FMDC estimator is compared to the performance of other known estimators using simulated and real data. [ABSTRACT FROM AUTHOR]

The product model is assumed to be an appropriate statistical model for multilook polarimetric synthetic radar data (PolSAR). According to this model, the observed signal is considered as the product of independent random variates of a complex Gaussian speckle and a non-Gaussian texture. With different texture distributions, the product model leads to different expressions for the compound distribution considered as an infinite mixture model. In this paper, the maximum-likeli-hood (ML) estimator is derived to jointly estimate the speckle and texture parameters in the compound distribution model using the multilook polarimetric radar data. In particular, we estimate: 1) the equivalent number of looks; 2) the covariance matrix of the speckle component; and 3) the texture distribution parameters. The expectation–maximization algorithm is developed to compute the ML estimates of the unknown parameters. The hybrid Cramer–Rao bounds (HCRBs) are also derived for these parameters. First, a general HCRB expression is derived under an arbitrary texture distribution. Then, this expression is simplified for a specific texture distribution. The performance of the ML is compared with the performance of other known estimators using the simulated and real multilook PolSAR data. For real data, a goodness of fit of multilook PolSAR data histograms is used to assess the fitting accuracy of the compound distributions using different estimators. [ABSTRACT FROM AUTHOR]