SLC-Domain SAR RFI Suppression via Sliding-Window Local Tensorization and Energy-Guided CUR Projection.
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| Title: | SLC-Domain SAR RFI Suppression via Sliding-Window Local Tensorization and Energy-Guided CUR Projection. |
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| Authors: | Guo, Qiang1 (AUTHOR), Tian, Yuhang1,2 (AUTHOR) tianyuhang01@hrbeu.edu.cn, Huang, Shuai1 (AUTHOR), Qi, Liangang1,2 (AUTHOR), Shulga, Sergiy2 (AUTHOR) |
| Source: | Remote Sensing. Feb2026, Vol. 18 Issue 4, p652. 31p. |
| Subjects: | Synthetic aperture radar, Radio interference, Interference suppression, Signal processing, Matrix decomposition |
| Abstract: | Highlights: What are the main findings? We propose an SLC-domain, post-focusing RFI mitigation framework, within which an alternating-projection algorithm is developed with sliding-window local tensorization and energy-guided tensor CUR projection as its core components. In semi-synthetic tests at SIR = −10/−20/−30, ETCUR-RSB_LTM achieves PCC = 0.9102/0.9066/0.9057 and zoom-in RRMSE (dB) = −26.5146/−25.1164/−24.9106, outperforming the comparison methods (RBSF and FIMD) with best-case gains of ΔPCC = +0.0207/+0.0394/+0.0376 and ΔRRMSE (dB)= 3.0028/3.2865/2.2265. What is the implication of the main finding? On real RFI-contaminated SLC data, ETCUR-RSB_LTM attains the lowest SBR = 0.1834 and the highest PhaseR = 0.9589 among the compared methods. The method is directly applicable to large-scale SLC post-processing, while CUR sampling under a fixed budget reduces the cost of low-rank estimation. Synthetic aperture radar (SAR) imaging is highly vulnerable to radio-frequency interference (RFI) in complex electromagnetic environments, which can introduce structured artifacts and obscure targets in single-look complex (SLC) products. Most existing suppression methods rely on separability along a single dimension or require interference-specific parameter tuning, limiting robustness under multidimensional coupling and strong scatterers. We propose a range-domain sliding-window local tensorization that rearranges SLC data into localized range–azimuth–block-index tensors to better expose multi-mode correlations. On this representation, an energy-guided tensor CUR low-rank projector is embedded into an alternating-projection scheme that alternates complex-valued soft-thresholding for the sparse scene-plus-noise term and CUR-based projection for the structured RFI term. The cleaned SLC image is obtained by de-tensorizing the estimated RFI component and subtracting it from the input SLC. Experiments on semi-synthetic data, where controlled RFI is superimposed on real SLC scenes, and on real Sentinel-1 SLC data containing RFI demonstrate improved Pearson correlation coefficient (PCC) and perceptual image quality while preserving target signatures and scene textures, particularly under strong interference and strong coupling. The proposed approach provides a practical SLC-domain RFI mitigation tool for post-focusing SAR products without requiring explicit interference parameterization. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We propose an SLC-domain, post-focusing RFI mitigation framework, within which an alternating-projection algorithm is developed with sliding-window local tensorization and energy-guided tensor CUR projection as its core components. In semi-synthetic tests at SIR = −10/−20/−30, ETCUR-RSB_LTM achieves PCC = 0.9102/0.9066/0.9057 and zoom-in RRMSE (dB) = −26.5146/−25.1164/−24.9106, outperforming the comparison methods (RBSF and FIMD) with best-case gains of ΔPCC = +0.0207/+0.0394/+0.0376 and ΔRRMSE (dB)= 3.0028/3.2865/2.2265. What is the implication of the main finding? On real RFI-contaminated SLC data, ETCUR-RSB_LTM attains the lowest SBR = 0.1834 and the highest PhaseR = 0.9589 among the compared methods. The method is directly applicable to large-scale SLC post-processing, while CUR sampling under a fixed budget reduces the cost of low-rank estimation. Synthetic aperture radar (SAR) imaging is highly vulnerable to radio-frequency interference (RFI) in complex electromagnetic environments, which can introduce structured artifacts and obscure targets in single-look complex (SLC) products. Most existing suppression methods rely on separability along a single dimension or require interference-specific parameter tuning, limiting robustness under multidimensional coupling and strong scatterers. We propose a range-domain sliding-window local tensorization that rearranges SLC data into localized range–azimuth–block-index tensors to better expose multi-mode correlations. On this representation, an energy-guided tensor CUR low-rank projector is embedded into an alternating-projection scheme that alternates complex-valued soft-thresholding for the sparse scene-plus-noise term and CUR-based projection for the structured RFI term. The cleaned SLC image is obtained by de-tensorizing the estimated RFI component and subtracting it from the input SLC. Experiments on semi-synthetic data, where controlled RFI is superimposed on real SLC scenes, and on real Sentinel-1 SLC data containing RFI demonstrate improved Pearson correlation coefficient (PCC) and perceptual image quality while preserving target signatures and scene textures, particularly under strong interference and strong coupling. The proposed approach provides a practical SLC-domain RFI mitigation tool for post-focusing SAR products without requiring explicit interference parameterization. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18040652 |
