Multilook Polarimetric SAR Change Detection Using Stochastic Distances Between Matrix-Variate Gd0 Distributions

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.