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Abstract Coregistration is among the most important and challenging tasks when dealing with multiple synthetic aperture radar (SAR) images, especially when they are acquired at different time instants and characterised by low signal to noise power ratio (SNR) that contributes to their coherence reduction. However, even if some technological expedients could be implemented to maintain the same trajectory and to compensate for these inaccuracies during the acquisition campaign, multitemporal SAR images always need additional registration refinements after compression. Usually, to coregister a series of multitemporal SAR images, one of them is selected as the master, and the remainders are separately registered to it. Differently, in this study, a new strategy is developed to jointly coregister a stack of multitemporal SAR images. It is based on the exploitation of the cross‐correlations in turn computed from each couple of cross‐correlations (a.k.a. cross‐cross‐correlations) of the extracted patches. By doing so, the method is capable of exploiting also the respective misregistration information between the slaves during the estimation process. In this respect, this methodology is applied to enhance the registration capabilities of the constrained Least Squares (CLS) optimisation method, which instead does not account for the reciprocal information related to the slaves. Several tests are performed on multitemporal airborne‐measured SAR data. Obtained results show the effectiveness of the proposed algorithm in terms of achieved root mean square error for images affected by respective rotations also in comparison with the CLS counterpart.

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.