Estimating the Coherency Matrices of Polarised and Depolarised Components of PolSAR Data.
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| Title: | Estimating the Coherency Matrices of Polarised and Depolarised Components of PolSAR Data. |
|---|---|
| Authors: | Ballester-Berman, J. David1 (AUTHOR) davidb@ua.es, Xie, Qinghua2 (AUTHOR), Shi, Hongtao3 (AUTHOR) |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 7, p1043. 35p. |
| Subjects: | Polarization (Electricity), Matrix decomposition, Polarimetric remote sensing, Matrices (Mathematics), Scattering (Physics), Mathematical models |
| Abstract: | Highlights: What are the main findings? The proposed decomposition estimates the coherency matrices of the polarised and depolarised components of PolSAR data on the basis of the 3-D Barakat degree of polarisation. The method performs consistently across multiple frequencies (P-, L-, C-band) and diverse vegetated targets (indoor measurements on short vegetation and airborne data on boreal forest), with decomposed scattering mechanisms aligning with established physical theory. What is the implication of the main finding? The method provides a framework that bridges model-free (i.e., the MF3C decomposition) and model-based approaches, conditioning the integration of diverse physical scattering models into the decomposition process according to the previous separation of polarised/depolarised components. Model-based polarimetric SAR (PolSAR) algorithms for bio- and geophysical parameter estimation rely on the effective separation of the combined scattering response of vegetation canopies and the soil surface through physically based models. However, the interpretation of polarimetric features derived from physical models is still subject to some ambiguity. Another strategy for complementing the model-based approaches for scattering mechanisms characterisation deals with the separation of the polarised and depolarised contributions of the PolSAR data according to their degree of polarisation. In this paper, we propose a two-component decomposition for estimating the depolarised and polarised components within the target and their corresponding coherency matrices. The method requires the previous calculation of the backscattering powers given by the model-free three-component (MF3C) decomposition, which in turn relies on the 3-D Barakat degree of polarisation. This quantitative information allows us to construct an inversion algorithm to retrieve the proportion of the polarised and depolarised contributions for all the elements of the observed coherency matrix under the reflection symmetry assumption. In essence, the proposed decomposition can be regarded as an extension of the MF3C method and, as a consequence, it enables the exploitation of both model-free and model-based approaches by using a physical rationale driven by the capability of the 3-D Barakat degree of polarisation. Therefore, practical applications can benefit from this approach as the retrieval of target parameters could presumably be done in a more accurate way by directly applying existing scattering models to both components. Indoor multi-frequency datasets acquired over three vegetation samples from the European Microwave Signature Laboratory (EMSL) and P-, L-, and C-band AIRSAR images over a boreal forest in Germany have been employed for testing the proposed decomposition. Performance analysis was performed using different polarimetric tools applied to the outcomes of the two-component decomposition, namely, the eigendecomposition and the copolar cross-correlation analysis of polarised and depolarised components, as well as histograms and a correlation analysis among backscattering powers. Overall, it has been observed that the method outputs are consistent with the theoretical expectations for the depolarised and polarised scattering components for a wide range of scenarios and sensor frequencies. [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.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 192958578 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Estimating the Coherency Matrices of Polarised and Depolarised Components of PolSAR Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ballester-Berman%2C+J%2E+David%22">Ballester-Berman, J. David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> davidb@ua.es</i><br /><searchLink fieldCode="AR" term="%22Xie%2C+Qinghua%22">Xie, Qinghua</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Hongtao%22">Shi, Hongtao</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Apr2026, Vol. 18 Issue 7, p1043. 35p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Polarization+%28Electricity%29%22">Polarization (Electricity)</searchLink><br /><searchLink fieldCode="DE" term="%22Matrix+decomposition%22">Matrix decomposition</searchLink><br /><searchLink fieldCode="DE" term="%22Polarimetric+remote+sensing%22">Polarimetric remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Matrices+%28Mathematics%29%22">Matrices (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Scattering+%28Physics%29%22">Scattering (Physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? The proposed decomposition estimates the coherency matrices of the polarised and depolarised components of PolSAR data on the basis of the 3-D Barakat degree of polarisation. The method performs consistently across multiple frequencies (P-, L-, C-band) and diverse vegetated targets (indoor measurements on short vegetation and airborne data on boreal forest), with decomposed scattering mechanisms aligning with established physical theory. What is the implication of the main finding? The method provides a framework that bridges model-free (i.e., the MF3C decomposition) and model-based approaches, conditioning the integration of diverse physical scattering models into the decomposition process according to the previous separation of polarised/depolarised components. Model-based polarimetric SAR (PolSAR) algorithms for bio- and geophysical parameter estimation rely on the effective separation of the combined scattering response of vegetation canopies and the soil surface through physically based models. However, the interpretation of polarimetric features derived from physical models is still subject to some ambiguity. Another strategy for complementing the model-based approaches for scattering mechanisms characterisation deals with the separation of the polarised and depolarised contributions of the PolSAR data according to their degree of polarisation. In this paper, we propose a two-component decomposition for estimating the depolarised and polarised components within the target and their corresponding coherency matrices. The method requires the previous calculation of the backscattering powers given by the model-free three-component (MF3C) decomposition, which in turn relies on the 3-D Barakat degree of polarisation. This quantitative information allows us to construct an inversion algorithm to retrieve the proportion of the polarised and depolarised contributions for all the elements of the observed coherency matrix under the reflection symmetry assumption. In essence, the proposed decomposition can be regarded as an extension of the MF3C method and, as a consequence, it enables the exploitation of both model-free and model-based approaches by using a physical rationale driven by the capability of the 3-D Barakat degree of polarisation. Therefore, practical applications can benefit from this approach as the retrieval of target parameters could presumably be done in a more accurate way by directly applying existing scattering models to both components. Indoor multi-frequency datasets acquired over three vegetation samples from the European Microwave Signature Laboratory (EMSL) and P-, L-, and C-band AIRSAR images over a boreal forest in Germany have been employed for testing the proposed decomposition. Performance analysis was performed using different polarimetric tools applied to the outcomes of the two-component decomposition, namely, the eigendecomposition and the copolar cross-correlation analysis of polarised and depolarised components, as well as histograms and a correlation analysis among backscattering powers. Overall, it has been observed that the method outputs are consistent with the theoretical expectations for the depolarised and polarised scattering components for a wide range of scenarios and sensor frequencies. [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/rs18071043 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 35 StartPage: 1043 Subjects: – SubjectFull: Polarization (Electricity) Type: general – SubjectFull: Matrix decomposition Type: general – SubjectFull: Polarimetric remote sensing Type: general – SubjectFull: Matrices (Mathematics) Type: general – SubjectFull: Scattering (Physics) Type: general – SubjectFull: Mathematical models Type: general Titles: – TitleFull: Estimating the Coherency Matrices of Polarised and Depolarised Components of PolSAR Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ballester-Berman, J. David – PersonEntity: Name: NameFull: Xie, Qinghua – PersonEntity: Name: NameFull: Shi, Hongtao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 7 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
