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Forest Height and Underlying Topography Inversion Using Polarimetric SAR Tomography Based on SKP Decomposition and Maximum Likelihood Estimation

School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
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Forests 2021, 12(4), 444; https://doi.org/10.3390/f12040444
Received: 26 December 2020 / Revised: 20 March 2021 / Accepted: 2 April 2021 / Published: 6 April 2021
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
The key point of forest height and underlying topography inversion using synthetic aperture radar tomography (TomoSAR) depends on the accurate positioning of the phase centers of different scattering mechanisms. The traditional nonparametric spectrum analysis methods (such as beamforming and Capon) have limited vertical resolution and cannot accurately distinguish closely spaced scatterers. In addition, it is very difficult to accurately estimate the ground or canopy heights with single polarimetric SAR images because there is no guarantee that the vertical profile will generate two clear and separate peaks for all resolution cells. A polarimetric TomoSAR method based on SKP (sum of Kronecker products) decomposition and iterative maximum likelihood estimation is proposed in this paper. On the one hand, the iterative maximum likelihood TomoSAR method has a higher vertical resolution than that of the traditional methods. On the other hand, the separation of the canopy scattering mechanism and the ground scattering mechanism is conducive to the positioning of the phase centers. This method was applied to the inversion of forest height and underlying topography in a tropical forest over the TropiSAR2009 test site in Paracou, French Guiana with six passes of polarimetric SAR images. The inversion accuracy of underlying topography of the proposed method was up to 1.489 m and the inversion accuracy of forest height was up to 1.765 m. Compared with the traditional polarimetric beamforming and polarimetric capon methods, the proposed method greatly improved the inversion accuracy of forest height and underlying topography. View Full-Text
Keywords: forest height; underlying topography; maximum likelihood estimation; SKP decomposition; polarimetric SAR tomography forest height; underlying topography; maximum likelihood estimation; SKP decomposition; polarimetric SAR tomography
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MDPI and ACS Style

Wan, J.; Wang, C.; Shen, P.; Hu, J.; Fu, H.; Zhu, J. Forest Height and Underlying Topography Inversion Using Polarimetric SAR Tomography Based on SKP Decomposition and Maximum Likelihood Estimation. Forests 2021, 12, 444. https://doi.org/10.3390/f12040444

AMA Style

Wan J, Wang C, Shen P, Hu J, Fu H, Zhu J. Forest Height and Underlying Topography Inversion Using Polarimetric SAR Tomography Based on SKP Decomposition and Maximum Likelihood Estimation. Forests. 2021; 12(4):444. https://doi.org/10.3390/f12040444

Chicago/Turabian Style

Wan, Jie, Changcheng Wang, Peng Shen, Jun Hu, Haiqiang Fu, and Jianjun Zhu. 2021. "Forest Height and Underlying Topography Inversion Using Polarimetric SAR Tomography Based on SKP Decomposition and Maximum Likelihood Estimation" Forests 12, no. 4: 444. https://doi.org/10.3390/f12040444

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