Physics-Aware Deep Learning for SAR and InSAR Remote Sensing: Models, Methods, and Open Challenges.
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| Title: | Physics-Aware Deep Learning for SAR and InSAR Remote Sensing: Models, Methods, and Open Challenges. |
|---|---|
| Authors: | Taricco, Giorgio1 (AUTHOR) |
| Source: | Remote Sensing. Aug2026, Vol. 18 Issue 15, p2567. 34p. |
| Subjects: | Synthetic aperture radar, Radar interferometry, Microwave remote sensing, Remote sensing |
| Abstract: | Highlights: What are the main findings? This review provides a systematic integration and extension of existing physical constraint dimensions for Synthetic Aperture Radar (SAR) and Interferometric Synthetic Aperture Radar (InSAR) learning, organized according to radar-specific constraints and algorithmic integration modes. The review shows that embedding radar physics improves interpretability, robustness, and consistency with sensing geometry, and two illustrative case studies—one for sparse SAR imaging and one for InSAR phase estimation—show how physics-aware designs compare with both black-box learning and classical structure-preserving baselines. What are the implications of the main findings? Future learning methods for SAR and InSAR remote sensing should incorporate sensing physics, coherent scattering structure, and acquisition geometry rather than treating radar data as generic images. The paper provides a structured remote sensing perspective for developing more reliable, physically interpretable, and data-efficient learning methods for radar Earth observation. SAR and InSAR are fundamental sensing modalities for all-weather, day-and-night Earth observation because they operate independently of solar illumination and retain sensitivity to scene structure under conditions that often limit optical imaging. Recent Deep Learning (DL) methods have improved SAR image interpretation, inverse imaging, target recognition, and InSAR-based deformation analysis, but many purely data-driven pipelines still neglect the forward sensing model, coherent scattering physics, speckle statistics, phase structure, and acquisition geometry that shape radar observations. This paper develops a physics-aware perspective on learning for SAR and InSAR. The literature is organized along two complementary axes: the physical constraint dimensions that govern radar measurements, namely polarization, scattering, signal-domain structure, resolution, and interferometric phase/coherence, and the integration modes through which such structure enters modern learning systems, including representation design, model-guided architectures, and learning-assisted inverse problems. We further discuss representative applications, dataset and evaluation issues, domain-shift challenges, and trustworthy deployment. Finally, two constructive illustrative case studies are included: one on sparse SAR imaging with an explicit SAR sensing matrix, and one on InSAR phase-domain estimation under wrapped phase, coherence variation, and physics-aware regularization. Together, they illustrate how physics-aware learning compares with classical structure-preserving baselines and unconstrained black-box learning in representative amplitude-driven and phase-driven inverse settings. [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: 196272247 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Physics-Aware Deep Learning for SAR and InSAR Remote Sensing: Models, Methods, and Open Challenges. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Taricco%2C+Giorgio%22">Taricco, Giorgio</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Aug2026, Vol. 18 Issue 15, p2567. 34p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Synthetic+aperture+radar%22">Synthetic aperture radar</searchLink><br /><searchLink fieldCode="DE" term="%22Radar+interferometry%22">Radar interferometry</searchLink><br /><searchLink fieldCode="DE" term="%22Microwave+remote+sensing%22">Microwave remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? This review provides a systematic integration and extension of existing physical constraint dimensions for Synthetic Aperture Radar (SAR) and Interferometric Synthetic Aperture Radar (InSAR) learning, organized according to radar-specific constraints and algorithmic integration modes. The review shows that embedding radar physics improves interpretability, robustness, and consistency with sensing geometry, and two illustrative case studies—one for sparse SAR imaging and one for InSAR phase estimation—show how physics-aware designs compare with both black-box learning and classical structure-preserving baselines. What are the implications of the main findings? Future learning methods for SAR and InSAR remote sensing should incorporate sensing physics, coherent scattering structure, and acquisition geometry rather than treating radar data as generic images. The paper provides a structured remote sensing perspective for developing more reliable, physically interpretable, and data-efficient learning methods for radar Earth observation. SAR and InSAR are fundamental sensing modalities for all-weather, day-and-night Earth observation because they operate independently of solar illumination and retain sensitivity to scene structure under conditions that often limit optical imaging. Recent Deep Learning (DL) methods have improved SAR image interpretation, inverse imaging, target recognition, and InSAR-based deformation analysis, but many purely data-driven pipelines still neglect the forward sensing model, coherent scattering physics, speckle statistics, phase structure, and acquisition geometry that shape radar observations. This paper develops a physics-aware perspective on learning for SAR and InSAR. The literature is organized along two complementary axes: the physical constraint dimensions that govern radar measurements, namely polarization, scattering, signal-domain structure, resolution, and interferometric phase/coherence, and the integration modes through which such structure enters modern learning systems, including representation design, model-guided architectures, and learning-assisted inverse problems. We further discuss representative applications, dataset and evaluation issues, domain-shift challenges, and trustworthy deployment. Finally, two constructive illustrative case studies are included: one on sparse SAR imaging with an explicit SAR sensing matrix, and one on InSAR phase-domain estimation under wrapped phase, coherence variation, and physics-aware regularization. Together, they illustrate how physics-aware learning compares with classical structure-preserving baselines and unconstrained black-box learning in representative amplitude-driven and phase-driven inverse settings. [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/rs18152567 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 34 StartPage: 2567 Subjects: – SubjectFull: Synthetic aperture radar Type: general – SubjectFull: Radar interferometry Type: general – SubjectFull: Microwave remote sensing Type: general – SubjectFull: Remote sensing Type: general Titles: – TitleFull: Physics-Aware Deep Learning for SAR and InSAR Remote Sensing: Models, Methods, and Open Challenges. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Taricco, Giorgio IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 15 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
