A Novel High-Resolution Fully Polarization Nonparametric Spectrum Estimation Method for Forest SAR Tomography
The polarimetric synthetic aperture radar tomography (TomoSAR) technique has proven to be a highly promising cutting-edge microwave remote sensing technique for obtaining forest vertical structure parameters because of its ability in three-dimensional imaging. For distributed scatterers like forests...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10988680/ |
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| author | Youjun Wang Xing Peng Qinghua Xie Yanan Du Bing Zhang Xiaomin Luo Weijun Yan Xinwu Li |
| author_facet | Youjun Wang Xing Peng Qinghua Xie Yanan Du Bing Zhang Xiaomin Luo Weijun Yan Xinwu Li |
| author_sort | Youjun Wang |
| collection | DOAJ |
| description | The polarimetric synthetic aperture radar tomography (TomoSAR) technique has proven to be a highly promising cutting-edge microwave remote sensing technique for obtaining forest vertical structure parameters because of its ability in three-dimensional imaging. For distributed scatterers like forests, tomograms are generally estimated from the polarimetric covariance matrix. And the estimation accuracy of the polarimetric covariance matrix directly determines the accuracy of parameter inversion. However, most of the current covariance estimation methods are relatively simple, and it is difficult to ensure their estimation accuracy. Moreover, most current tomographic focusing estimators suffer from low elevation resolution and are vulnerable to sidelobe interference, leading to inadequate spectral estimation and parameter extraction performance. Based on this, this article first proposes a high-resolution fully polarimetric nonparametric spectrum estimation algorithm with low sidelobes. In this method, the polarimetric covariance matrix of the target pixel is estimated by searching its homogeneous pixels in the neighborhood based on the nonlocal means method, and the interference caused by the neighborhood pixel with great difference in statistical characteristics is eliminated as far as possible. Then, the obtained covariance matrix is reconstructed by eigenvalue decomposition to enhance the real reflected signal, and reduce sidelobe. To validate the proposed method in this article, we perform experimental verification and accuracy evaluation using TropiSAR 2009 fully polarized airborne data collected in French Guiana. The results demonstrate that the new method has more advantages than other methods in forest tomographic focusing and parameters estimation. |
| format | Article |
| id | doaj-art-c7fed70f72a74df8b20c61142d4d47fa |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-c7fed70f72a74df8b20c61142d4d47fa2025-08-20T03:21:50ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118128821289610.1109/JSTARS.2025.356571710988680A Novel High-Resolution Fully Polarization Nonparametric Spectrum Estimation Method for Forest SAR TomographyYoujun Wang0https://orcid.org/0000-0003-2461-2477Xing Peng1https://orcid.org/0000-0001-9240-6347Qinghua Xie2https://orcid.org/0000-0003-4293-3354Yanan Du3https://orcid.org/0000-0003-1277-564XBing Zhang4https://orcid.org/0000-0003-2093-2975Xiaomin Luo5https://orcid.org/0000-0003-0439-4978Weijun Yan6Xinwu Li7https://orcid.org/0000-0002-0953-3618School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, ChinaSchool of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, ChinaSchool of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, ChinaSchool of Geographical Sciences, Guangzhou University, Guangzhou, ChinaSchool of Geomatics, Liaoning Technical University, Fuxin, ChinaSchool of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, ChinaHainan Provincial Ecological and Environmental Monitoring Centre, Haikou, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaThe polarimetric synthetic aperture radar tomography (TomoSAR) technique has proven to be a highly promising cutting-edge microwave remote sensing technique for obtaining forest vertical structure parameters because of its ability in three-dimensional imaging. For distributed scatterers like forests, tomograms are generally estimated from the polarimetric covariance matrix. And the estimation accuracy of the polarimetric covariance matrix directly determines the accuracy of parameter inversion. However, most of the current covariance estimation methods are relatively simple, and it is difficult to ensure their estimation accuracy. Moreover, most current tomographic focusing estimators suffer from low elevation resolution and are vulnerable to sidelobe interference, leading to inadequate spectral estimation and parameter extraction performance. Based on this, this article first proposes a high-resolution fully polarimetric nonparametric spectrum estimation algorithm with low sidelobes. In this method, the polarimetric covariance matrix of the target pixel is estimated by searching its homogeneous pixels in the neighborhood based on the nonlocal means method, and the interference caused by the neighborhood pixel with great difference in statistical characteristics is eliminated as far as possible. Then, the obtained covariance matrix is reconstructed by eigenvalue decomposition to enhance the real reflected signal, and reduce sidelobe. To validate the proposed method in this article, we perform experimental verification and accuracy evaluation using TropiSAR 2009 fully polarized airborne data collected in French Guiana. The results demonstrate that the new method has more advantages than other methods in forest tomographic focusing and parameters estimation.https://ieeexplore.ieee.org/document/10988680/Forest heightfully polarizationlow sidelobesnonlocal meansTomoSARunderlying topography |
| spellingShingle | Youjun Wang Xing Peng Qinghua Xie Yanan Du Bing Zhang Xiaomin Luo Weijun Yan Xinwu Li A Novel High-Resolution Fully Polarization Nonparametric Spectrum Estimation Method for Forest SAR Tomography IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Forest height fully polarization low sidelobes nonlocal means TomoSAR underlying topography |
| title | A Novel High-Resolution Fully Polarization Nonparametric Spectrum Estimation Method for Forest SAR Tomography |
| title_full | A Novel High-Resolution Fully Polarization Nonparametric Spectrum Estimation Method for Forest SAR Tomography |
| title_fullStr | A Novel High-Resolution Fully Polarization Nonparametric Spectrum Estimation Method for Forest SAR Tomography |
| title_full_unstemmed | A Novel High-Resolution Fully Polarization Nonparametric Spectrum Estimation Method for Forest SAR Tomography |
| title_short | A Novel High-Resolution Fully Polarization Nonparametric Spectrum Estimation Method for Forest SAR Tomography |
| title_sort | novel high resolution fully polarization nonparametric spectrum estimation method for forest sar tomography |
| topic | Forest height fully polarization low sidelobes nonlocal means TomoSAR underlying topography |
| url | https://ieeexplore.ieee.org/document/10988680/ |
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