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A Forest Height Joint Inversion Method Using Multibaseline PolInSAR Data
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DOI：10.1109/LGRS.2022.3222572.png)
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摘要
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Estimating vegetation height from polarimetric interferometric synthetic aperture radar (PolInSAR) data using the random volume over ground (RVoG) model has long been used. Most of these methods propose models and apply them to real airborne data to demonstrate their potential. The single-baseline PolInSAR forest height estimation based on the RVoG model lacks sufficient observation information. For this reason, multibaseline data are introduced to address this. This letter fits the relationship of model parameters in multibaseline observation scenarios and focuses the forest height inversion on the calculation of pure volume decorrelation. Subsequently, a multibaseline forest height joint inversion method based on the least-squares principle is adopted. Finally, we use airborne PolInSAR data from the Lope and Mondah sites collected by uninhabited aerial vehicle synthetic aperture radar (UAVSAR) and F airborne synthetic aperture radar (F-SAR) systems during AfriSAR 2016 to verify the proposed method. The experimental results show that the accuracy of the proposed method (Lope: root mean square error (RMSE) = 5.8 m, Mondah: RMSE = 5.12 m) is 38.1% and 34.53% higher than the coherence separation product (Lope: RMSE = 9.37 m, Mondah: RMSE = 7.82 m).
Keyword:
Forestry
Decorrelation
Coherence
Data models
Estimation
Synthetic aperture radar
Solid modeling
Forest height estimation
least-squares principle
multibaseline polarimetric interferometric synthetic aperture radar (PolInSAR)
random volume over ground (RVoG) model
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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A Novel Iterative Reweighted Method for Forest Height Inversion Using Multibaseline PolInSAR Data
PREAI
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