Forest Height and Underlying Topography Inversion Using Polarimetric SAR Tomography Based on SKP Decomposition and Maximum Likelihood Estimation
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)...
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doaj-53f0bffeb1314b25a3b46935a552dc712021-04-06T23:03:37ZengMDPI AGForests1999-49072021-04-011244444410.3390/f12040444Forest Height and Underlying Topography Inversion Using Polarimetric SAR Tomography Based on SKP Decomposition and Maximum Likelihood EstimationJie Wan0Changcheng Wang1Peng Shen2Jun Hu3Haiqiang Fu4Jianjun Zhu5School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaThe 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.https://www.mdpi.com/1999-4907/12/4/444forest heightunderlying topographymaximum likelihood estimationSKP decompositionpolarimetric SAR tomography |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jie Wan Changcheng Wang Peng Shen Jun Hu Haiqiang Fu Jianjun Zhu |
spellingShingle |
Jie Wan Changcheng Wang Peng Shen Jun Hu Haiqiang Fu Jianjun Zhu Forest Height and Underlying Topography Inversion Using Polarimetric SAR Tomography Based on SKP Decomposition and Maximum Likelihood Estimation Forests forest height underlying topography maximum likelihood estimation SKP decomposition polarimetric SAR tomography |
author_facet |
Jie Wan Changcheng Wang Peng Shen Jun Hu Haiqiang Fu Jianjun Zhu |
author_sort |
Jie Wan |
title |
Forest Height and Underlying Topography Inversion Using Polarimetric SAR Tomography Based on SKP Decomposition and Maximum Likelihood Estimation |
title_short |
Forest Height and Underlying Topography Inversion Using Polarimetric SAR Tomography Based on SKP Decomposition and Maximum Likelihood Estimation |
title_full |
Forest Height and Underlying Topography Inversion Using Polarimetric SAR Tomography Based on SKP Decomposition and Maximum Likelihood Estimation |
title_fullStr |
Forest Height and Underlying Topography Inversion Using Polarimetric SAR Tomography Based on SKP Decomposition and Maximum Likelihood Estimation |
title_full_unstemmed |
Forest Height and Underlying Topography Inversion Using Polarimetric SAR Tomography Based on SKP Decomposition and Maximum Likelihood Estimation |
title_sort |
forest height and underlying topography inversion using polarimetric sar tomography based on skp decomposition and maximum likelihood estimation |
publisher |
MDPI AG |
series |
Forests |
issn |
1999-4907 |
publishDate |
2021-04-01 |
description |
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. |
topic |
forest height underlying topography maximum likelihood estimation SKP decomposition polarimetric SAR tomography |
url |
https://www.mdpi.com/1999-4907/12/4/444 |
work_keys_str_mv |
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