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. |
|---|---|
| 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] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 191972737 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: SLC-Domain SAR RFI Suppression via Sliding-Window Local Tensorization and Energy-Guided CUR Projection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Guo%2C+Qiang%22">Guo, Qiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tian%2C+Yuhang%22">Tian, Yuhang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> tianyuhang01@hrbeu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Shuai%22">Huang, Shuai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qi%2C+Liangang%22">Qi, Liangang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shulga%2C+Sergiy%22">Shulga, Sergiy</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Feb2026, Vol. 18 Issue 4, p652. 31p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Synthetic+aperture+radar%22">Synthetic aperture radar</searchLink><br /><searchLink fieldCode="DE" term="%22Radio+interference%22">Radio interference</searchLink><br /><searchLink fieldCode="DE" term="%22Interference+suppression%22">Interference suppression</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Matrix+decomposition%22">Matrix decomposition</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=191972737 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18040652 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 652 Subjects: – SubjectFull: Synthetic aperture radar Type: general – SubjectFull: Radio interference Type: general – SubjectFull: Interference suppression Type: general – SubjectFull: Signal processing Type: general – SubjectFull: Matrix decomposition Type: general Titles: – TitleFull: SLC-Domain SAR RFI Suppression via Sliding-Window Local Tensorization and Energy-Guided CUR Projection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Guo, Qiang – PersonEntity: Name: NameFull: Tian, Yuhang – PersonEntity: Name: NameFull: Huang, Shuai – PersonEntity: Name: NameFull: Qi, Liangang – PersonEntity: Name: NameFull: Shulga, Sergiy IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 4 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
