From Single-Look to Multi-Temporal SAR Despeckling: A Latent-Space Guided Transfer Learning Approach.
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| Title: | From Single-Look to Multi-Temporal SAR Despeckling: A Latent-Space Guided Transfer Learning Approach. |
|---|---|
| Authors: | Pan, Baojing1 (AUTHOR), Yu, Ze1,2 (AUTHOR), Yao, Xianxun1 (AUTHOR) xianxun.yao@buaa.edu.cn, Tian, Zhiqiang1,2 (AUTHOR), Ren, Wei2 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1402. 22p. |
| Subjects: | Knowledge transfer, Synthetic aperture radar, Speckle interferometry, Convolutional neural networks, Time series analysis, Deep learning |
| Abstract: | Highlights: What are the main findings? A latent-space guided transfer learning framework (LGT-SAR) is introduced to explicitly bridge 2D single-look despeckling and 3D spatio-temporal modeling via latent-space regularization, enabling effective knowledge transfer for multi-temporal SAR despeckling. The proposed method achieves stronger detail and edge preservation while despeckling. What are the implications of the main findings? The approach provides a practical way to train robust multi-temporal despeckling models under limited multi-temporal samples by reusing mature single-image priors, mitigating over-smoothing and structural distortion. The fully convolutional design supports variable-length temporal inputs, improving adaptability to different acquisition/sampling conditions and facilitating deployment across diverse SAR time-series scenarios. Synthetic Aperture Radar (SAR) images are affected by speckle noise, which limits their application in fine object interpretation and quantitative analysis. Recent deep learning-based single-image SAR despeckling methods have made significant progress in spatial structure modeling but struggle to exploit temporal redundancy in multi-temporal data. Existing multi-temporal despeckling methods usually rely on complex spatiotemporal network structures, which are prone to overfitting or excessive smoothing of details when training samples are limited. To address these challenges, this paper proposes a latent-space-guided multi-temporal SAR despeckling method from the perspective of transfer learning and representation alignment, achieving effective knowledge transfer from single-image SAR despeckling to multi-temporal despeckling tasks. The method treats the single-image SAR despeckling task as a knowledge source domain, using stable latent space representations learned from the pre-trained single-image despeckling model as prior constraints. A latent space regularization mechanism is introduced during the training of the multi-temporal despeckling model, thereby establishing an explicit representation bridge between the 2D spatial model and the 3D spatiotemporal model. With this strategy, the multi-temporal model inherits the structural perception capability of the single-image model under limited training samples, improving speckle suppression while effectively maintaining image detail and structural consistency. Additionally, a pure convolutional network architecture is employed to support variable-length multi-temporal sequence input, enhancing the method's adaptability under different temporal sampling conditions. [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: 193715433 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: From Single-Look to Multi-Temporal SAR Despeckling: A Latent-Space Guided Transfer Learning Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pan%2C+Baojing%22">Pan, Baojing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Ze%22">Yu, Ze</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yao%2C+Xianxun%22">Yao, Xianxun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xianxun.yao@buaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Tian%2C+Zhiqiang%22">Tian, Zhiqiang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ren%2C+Wei%22">Ren, Wei</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 9, p1402. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Knowledge+transfer%22">Knowledge transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Synthetic+aperture+radar%22">Synthetic aperture radar</searchLink><br /><searchLink fieldCode="DE" term="%22Speckle+interferometry%22">Speckle interferometry</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A latent-space guided transfer learning framework (LGT-SAR) is introduced to explicitly bridge 2D single-look despeckling and 3D spatio-temporal modeling via latent-space regularization, enabling effective knowledge transfer for multi-temporal SAR despeckling. The proposed method achieves stronger detail and edge preservation while despeckling. What are the implications of the main findings? The approach provides a practical way to train robust multi-temporal despeckling models under limited multi-temporal samples by reusing mature single-image priors, mitigating over-smoothing and structural distortion. The fully convolutional design supports variable-length temporal inputs, improving adaptability to different acquisition/sampling conditions and facilitating deployment across diverse SAR time-series scenarios. Synthetic Aperture Radar (SAR) images are affected by speckle noise, which limits their application in fine object interpretation and quantitative analysis. Recent deep learning-based single-image SAR despeckling methods have made significant progress in spatial structure modeling but struggle to exploit temporal redundancy in multi-temporal data. Existing multi-temporal despeckling methods usually rely on complex spatiotemporal network structures, which are prone to overfitting or excessive smoothing of details when training samples are limited. To address these challenges, this paper proposes a latent-space-guided multi-temporal SAR despeckling method from the perspective of transfer learning and representation alignment, achieving effective knowledge transfer from single-image SAR despeckling to multi-temporal despeckling tasks. The method treats the single-image SAR despeckling task as a knowledge source domain, using stable latent space representations learned from the pre-trained single-image despeckling model as prior constraints. A latent space regularization mechanism is introduced during the training of the multi-temporal despeckling model, thereby establishing an explicit representation bridge between the 2D spatial model and the 3D spatiotemporal model. With this strategy, the multi-temporal model inherits the structural perception capability of the single-image model under limited training samples, improving speckle suppression while effectively maintaining image detail and structural consistency. Additionally, a pure convolutional network architecture is employed to support variable-length multi-temporal sequence input, enhancing the method's adaptability under different temporal sampling conditions. [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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18091402 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1402 Subjects: – SubjectFull: Knowledge transfer Type: general – SubjectFull: Synthetic aperture radar Type: general – SubjectFull: Speckle interferometry Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: From Single-Look to Multi-Temporal SAR Despeckling: A Latent-Space Guided Transfer Learning Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pan, Baojing – PersonEntity: Name: NameFull: Yu, Ze – PersonEntity: Name: NameFull: Yao, Xianxun – PersonEntity: Name: NameFull: Tian, Zhiqiang – PersonEntity: Name: NameFull: Ren, Wei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 9 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
