Dominant Scattering Mechanism Identification From Quad-Pol-SAR Data Analysis

Polarimetric decompositions are used to separate scatterers and identify their physical parameters by analyzing backscattering, coherence, or covariance matrices. Each cell within polarimetric synthetic aperture radar data is seen as a coherent or incoherent combination of different scattering mechanisms. However, targets are not perfectly characterized by these matrices due to the presence of noise components. The main objective of this study is to remedy the latest issue through proper noise effect elimination. Hence, we propose the reestimation of the coherence matrix, by incorporating a processing phase that searches for the number of elementary scattering mechanisms in each cell. This first step is based on the eigenvalues, which exploit the advantage of polarization basis independent of the eigenvectors. In the second step, a reduced space is defined by the eigenvectors selected, according to the cases of the first step, as those contributing to the construction of the target, excluding those judged to contribute to noise. The characteristic vector and the coherence matrix of the average target are then reconstructed in this new space in three different ways: summation of the elementary coherence matrices, applying Bernoulli's probability law, and orthogonal projection on the reduced space. Finally, the Freeman–Durden polarimetric decomposition and the $H$–$\overline{\alpha }$ Wishart classification are used to show the effectiveness of the process in terms of dominant scattering mechanism identification. Their application on simulated data and on fully polarized RadarSat-2 images of the city of Algiers attests to the performance of the proposed methodology to improve the identification of dominant scattering mechanisms.