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

Nizar Bouhlel (1, 2) , Stephane Meric (1)

1 IETR - Institut d'Électronique et des Technologies du numéRique
2 CentraleSupélec

In this article, we propose an efficient heterogeneous change detection algorithm based on stochastic distance measure between two G 0 d distributions. Due to its flexibility and simplicity, the matrix-variate G 0 d distribution has been successfully used to model the multilook polarimetric synthetic aperture radar (PolSAR) data and has been tested for classification, segmen-tation, and image analysis. Concretely, closed-form expressions for the Kullback-Leibler, Rényi of order β, Bhattacharyya, and Hellinger distances are provided to compute the stochastic distance between G 0 d distributions. In this context, we resort to the expectation-maximization (EM) to estimate accurately with low complexity 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. Index Terms-Bhattacharyya and Hellinger distances, change detection, expectation-maximization (EM) algorithm, multilook polarimetric synthetic aperture radar (PolSAR) data, Rényi of order β, stochastic distances: Kullback-Leibler.

Domains: Engineering Sciences [physics]

Nizar Bouhlel, Stephane Meric. Multilook Polarimetric SAR Change Detection Using Stochastic Distances Between Matrix-Variate Gd⁰ Distributions. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58 (10), pp.6823-6843. ⟨10.1109/TGRS.2020.2976766⟩. ⟨hal-02948490⟩