An Improved Sparse Bayesian Learning SAR Tomography Method and its Application for Forest Vertical Structure Inversion
Synthetic aperture radar tomography (TomoSAR) is widely used in reconstructing forest vertical structure, but accurately locating both ground and canopy scatterers in dense forest areas remains challenging. In this article, a novel sparse Bayesian learning (SBL) based TomoSAR method is proposed to a...
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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/11008668/ |
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| author | Jie Wan Changcheng Wang Peng Shen Yonghui Wei |
| author_facet | Jie Wan Changcheng Wang Peng Shen Yonghui Wei |
| author_sort | Jie Wan |
| collection | DOAJ |
| description | Synthetic aperture radar tomography (TomoSAR) is widely used in reconstructing forest vertical structure, but accurately locating both ground and canopy scatterers in dense forest areas remains challenging. In this article, a novel sparse Bayesian learning (SBL) based TomoSAR method is proposed to achieve super-resolution reconstruction of forest vertical structure. Two important improvements are considered in the process of SBL SAR tomography. First, a hybrid sparse basis is employed to accurately transform and reconstruct the forest vertical structure profile with different scattering mechanisms. Second, an adaptive singular value decomposition method is employed to address the instability issue caused by ill-conditioned inversion in Bayesian inference. Furthermore, leveraging high-resolution TomoSAR profiles significantly enhances the performance of forest vertical structure parameter inversion. The effectiveness of the proposed method is validated using multibaseline P-band airborne SAR images acquired in tropical forests at two distinct test sites. The results demonstrate that the proposed method achieved high-resolution SAR tomography imaging outcomes even within a limited baseline span. In terms of forest structure parameter inversion, the root mean square error (RMSE) of inverted forest height is 2.58 and 4.16 m compared to LiDAR measurements, while the RMSE of inverted underlying topography is 1.77 and 5.49 m. The proposed method is instrumental in retrieving large-scale forest structure parameters, particularly in preparation for the upcoming BIOMASS mission. |
| format | Article |
| id | doaj-art-e9ca75e281f44555b414babd851b0d2a |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-e9ca75e281f44555b414babd851b0d2a2025-08-20T03:26:09ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118137011371810.1109/JSTARS.2025.357219811008668An Improved Sparse Bayesian Learning SAR Tomography Method and its Application for Forest Vertical Structure InversionJie Wan0Changcheng Wang1https://orcid.org/0000-0003-4461-068XPeng Shen2Yonghui Wei3School of Geosciences and Info-Physics, Central South University, Changsha, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha, ChinaSynthetic aperture radar tomography (TomoSAR) is widely used in reconstructing forest vertical structure, but accurately locating both ground and canopy scatterers in dense forest areas remains challenging. In this article, a novel sparse Bayesian learning (SBL) based TomoSAR method is proposed to achieve super-resolution reconstruction of forest vertical structure. Two important improvements are considered in the process of SBL SAR tomography. First, a hybrid sparse basis is employed to accurately transform and reconstruct the forest vertical structure profile with different scattering mechanisms. Second, an adaptive singular value decomposition method is employed to address the instability issue caused by ill-conditioned inversion in Bayesian inference. Furthermore, leveraging high-resolution TomoSAR profiles significantly enhances the performance of forest vertical structure parameter inversion. The effectiveness of the proposed method is validated using multibaseline P-band airborne SAR images acquired in tropical forests at two distinct test sites. The results demonstrate that the proposed method achieved high-resolution SAR tomography imaging outcomes even within a limited baseline span. In terms of forest structure parameter inversion, the root mean square error (RMSE) of inverted forest height is 2.58 and 4.16 m compared to LiDAR measurements, while the RMSE of inverted underlying topography is 1.77 and 5.49 m. The proposed method is instrumental in retrieving large-scale forest structure parameters, particularly in preparation for the upcoming BIOMASS mission.https://ieeexplore.ieee.org/document/11008668/Forest heightforest vertical structuresparse Bayesian learning (SBL)synthetic aperture radar tomography (TomoSAR)underlying topography |
| spellingShingle | Jie Wan Changcheng Wang Peng Shen Yonghui Wei An Improved Sparse Bayesian Learning SAR Tomography Method and its Application for Forest Vertical Structure Inversion IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Forest height forest vertical structure sparse Bayesian learning (SBL) synthetic aperture radar tomography (TomoSAR) underlying topography |
| title | An Improved Sparse Bayesian Learning SAR Tomography Method and its Application for Forest Vertical Structure Inversion |
| title_full | An Improved Sparse Bayesian Learning SAR Tomography Method and its Application for Forest Vertical Structure Inversion |
| title_fullStr | An Improved Sparse Bayesian Learning SAR Tomography Method and its Application for Forest Vertical Structure Inversion |
| title_full_unstemmed | An Improved Sparse Bayesian Learning SAR Tomography Method and its Application for Forest Vertical Structure Inversion |
| title_short | An Improved Sparse Bayesian Learning SAR Tomography Method and its Application for Forest Vertical Structure Inversion |
| title_sort | improved sparse bayesian learning sar tomography method and its application for forest vertical structure inversion |
| topic | Forest height forest vertical structure sparse Bayesian learning (SBL) synthetic aperture radar tomography (TomoSAR) underlying topography |
| url | https://ieeexplore.ieee.org/document/11008668/ |
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