Interferometric phase noise governs the accuracies of both subsequent interferometric synthetic aperture radar (InSAR) data processing and the final measurements. Filtering is the main method for reducing the phase noise of InSAR. However, the traditional filtering algorithms operating in the spatial or transform domains often fail to reconcile effective noise suppression with the preservation of fine fringe details. Besides, the existing deep learning-based denoising methods still exhibit limitations, such as excessive smoothing, generation of spurious fringes, and redundant computations. To address these issues, this letter proposes a novel denoising network model, i.e., multibranch dilated residual network (MDRNet). It integrates dilated convolutions with a channel attention mechanism, achieving adaptive modulation of the receptive field, thereby effectively preserving fringe edges and authentic deformation signals while suppressing spurious fringes in decorrelated regions. Both simulation and real experiment results indicate that MDRNet attains superior performance under conditions of strong noise and low coherence. It achieves a more favorable balance between denoising efficacy and phase fidelity compared to existing methods. The corresponding test code is available at https://github.com/xiangHQ/MDRNet.git
Hongquan Xiang, Xue Cheng, Qicai Shi et al.· IEEE Geoscience and Remote S...· 0 citations
Abstract. Interferometric SAR (InSAR) techniques, particularly Persistent Scatterer Interferometry (PSI), have become essential for land deformation monitoring. However, phase unwrapping errors significantly impact the reliability of deformation measurements, especially in areas with high phase noise or rapid deformation. This paper introduces a back-to-back (B2B) approach that enhances PSI results through a direct phase integration of consecutive interferograms, combined with adaptive phase unwrapping error detection and correction. The methodology integrates coherence-based pixel selection, adaptive phase unwrapping with variable thresholds, spatial-temporal consistency checking, and interpolation. Validation across several mining sites confirms the method’s robustness. For example, at the Relave Talabre mine in Chile, the B2B approach increased measurement density by over 20% with respect to standard PSI, and improved temporal consistency between ascending and descending datasets. These results demonstrate that the B2B approach offers enhanced spatial coverage, more reliable phase unwrapping, and operational efficiency for large-scale deformation monitoring in mining environments.
Q. Gao, M. Crosetto, O. Monserrat et al.· The International Archives o...· 0 citations
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