Interferometric synthetic aperture radar (InSAR) image coregistration is a nontrivial task for its skew and distorted image pair, especially in severe decorrelated areas. In this letter, a new InSAR coregistration method, which considers both the coherence of the reference points and their topographical distribution, is proposed to perform accurate coregistration. First, a conventional cross-correlation registration method is performed, and a number of high coherent points are extracted as the reference points. Then, some well-distributed reference points are selected by utilizing the Voronoi diagram-based distribution optimization algorithm. Their subpixel correspondences are selected by finding the maximum values of the cross-correlation functions. Some singular correspondences are rejected by the random sample consensus method. Based on these accurate correspondences, the parameters of polynomial mapping function are estimated via the weighted least squares method. It improves the accuracy of geometrical mapping functions and overcomes the limitation of the conventional registration method when the reference points locate in low coherent area. C-band airborne repeat-pass acquired SAR data with 0.5-m resolution are used to validate the proposed algorithm by comparing with the conventional registration algorithm in accuracy. Experimental results prove the effectiveness of the proposed algorithm.

An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double- $l_1$ norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes.