A Simple RVoG Test for PolInSAR Data

Ballester-Berman, J. David; Vicente-Guijalba, Fernando; López-Sánchez, Juan M.

doi:10.1109/jstars.2014.2379438

Cited by 18 publications

(12 citation statements)

References 48 publications

(67 reference statements)

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“…At P band, precious works have shown that the volume scattering contribution is polarization-dependent since the orientation effect has been observed over forests [14,15,23]. As a result, this cannot meet the RVoG assumption and may induce biases of ground phase estimated by the three-stage inversion procedure [23].…”

Section: Coherence Locus Of Gvbmentioning

confidence: 99%

“…As a result, this cannot meet the RVoG assumption and may induce biases of ground phase estimated by the three-stage inversion procedure [23]. Nonetheless, the biases may be small and acceptable forest height results have been successfully extracted using the RVoG assumption at P band over the tropical and pine forests [24][25][26][27].…”

Section: Coherence Locus Of Gvbmentioning

confidence: 99%

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Estimation of Pine Forest Height and Underlying DEM Using Multi-Baseline P-Band PolInSAR Data

Wang

Zhu

et al. 2016

Remote Sensing

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On the basis of the Gaussian vertical backscatter (GVB) model, this paper proposes a new method for extracting pine forest height and forest underlying digital elevation model (FUDEM) from multi-baseline (MB) P-band polarimetric-interferometric radar (PolInSAR) data. Considering the linear ground-to-volume relationship, the GVB is linked to the interferometric coherences of different polarizations. Subsequently, an inversion algorithm, weighted complex least squares adjustment (WCLSA), is formulated, including the mathematical model, the stochastic model and the parameter estimation method. The WCLSA method can take full advantage of the redundant observations, adjust the contributions of different observations and avoid null ground-to-volume ratio (GVR) assumption. The simulated experiment demonstrates that the WCLSA method is feasible to estimate the pure ground and volume scattering contributions. Finally, the WCLSA method is applied to E-SAR P-band data acquired over Krycklan Catchment covered with mixed pine forest. It is shown that the FUDEM highly agrees with those derived by LiDAR, with a root mean square error (RMSE) of 3.45 m, improved by 23.0% in comparison to the three-stage method. The difference between the extracted forest height and LiDAR forest height is assessed with a RMSE of 1.45 m, improved by 37.5% and 26.0%, respectively, for model and inversion aspects in comparison to three-stage inversion based on random volume over ground (RVoG) model.

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Section: Coherence Locus Of Gvbmentioning

confidence: 99%

Section: Coherence Locus Of Gvbmentioning

confidence: 99%

Estimation of Pine Forest Height and Underlying DEM Using Multi-Baseline P-Band PolInSAR Data

Wang

Zhu

et al. 2016

Remote Sensing

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“…On account of the significant penetration of P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) systems, all polarimetric channels contain a non-ignorable ground contribution [1][2][3][4][5], generally leading to an underestimation of forest height in view of the Random Volume over Ground (RVoG) model [3,6]. So far, the RVoG model depicting the forest vertical backscatter profile with an exponential function has been widely taken advantage of in forest height estimation [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Moroever, when referring to temporal decorrelation [21][22][23][24][25], canopy filling [12,26] and terrain fluctuations [6,[27][28][29][30][31][32], some modified models were proposed to complement the RVoG model.…”

Section: Introductionmentioning

confidence: 99%

S-RVoG Model Inversion Based on Time-Frequency Optimization for P-Band Polarimetric SAR Interferometry

et al. 2019

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This paper investigates the potential of the time-frequency optimization on the basis of the sublook decomposition for forest height estimation. The optimization is deemed to be capable of extracting a relatively accurate volume contribution when P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) systems are adopted to observe forest-covered areas. The highest and the lowest phase centers acquired by the time-frequency optimization modify the conventional three-stage inversion process. This paper presents, for the first time, a performance assessment of the time-frequency optimization on P-band Pol-InSAR data over boreal forests. Simultaneously, to alleviate the model inversion errors caused by topographic fluctuations, forest height is estimated based on the sloped Random Volume over Ground (S-RVoG) model in which the incidence angle is corrected with the terrain slope. The E-SAR P-band Pol-InSAR data acquired during the BIOSAR 2008 campaign in Northern Sweden is utilized to evaluate the performance of the proposed method. From the results of the forest height estimation preprocessed with time-frequency optimization, the root mean square error (RMSE) of Random Volume over Ground (RVoG) and S-RVoG model on negative slope are 5.09 m and 4.71 m, respectively. It is concluded that the time-frequency processing and negative terrain slope compensation improve the inversion performance by 41 . 49 % and 11 . 96 % , respectively.

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“…The complex interferometric coherence of the observed PolInSAR data has been related to the vertical distribution of the vegetation scattering [10][11][12]. In a number of PolInSAR campaigns, the random volume over ground (RVoG) model [6,10] has been used to extract vegetation height from the complex interferometric coherence [15][16][17]. The RVoG model is a physical PolInSAR model that integrates the complex coherence and the biophysical parameters.…”

Section: Introductionmentioning

confidence: 99%

A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data

Lin

Zhu

et al. 2017

Applied Sciences

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Abstract:The random volume over ground (RVoG) model associates vegetation vertical structure parameters with multiple complex interferometric coherence observables. In this paper, on the basis of the RVoG model, a truncated singular value decomposition (TSVD)-based method is proposed for forest height inversion from single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. In addition, in order to improve the applicability of TSVD for this issue, a new truncation method is proposed for TSVD. Differing from the traditional three-stage method, the TSVD-based inversion method estimates the pure volume coherence directly from the complex interferometric coherence, and estimates the forest height from the estimated pure volume coherence with a least-squares method. As a result, the TSVD-based method can adjust the contributions of the polarizations in the estimation of the model parameters and avoid the null ground-to-volume ratio assumption. The simulated experiments undertaken in this study confirmed that the TSVD-based method performs better than the three-stage method in forest height inversion. The TSVD-based method was also applied to E-SAR P-band data acquired over the Krycklan Catchment, Sweden, which is covered with mixed pine forest. The results showed that the TSVD-based method improves the root-mean-square error by 48.6% when compared to the three-stage method, which further validates the performance of the TSVD-based method.

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A Simple RVoG Test for PolInSAR Data

Cited by 18 publications

References 48 publications

Estimation of Pine Forest Height and Underlying DEM Using Multi-Baseline P-Band PolInSAR Data

Estimation of Pine Forest Height and Underlying DEM Using Multi-Baseline P-Band PolInSAR Data

S-RVoG Model Inversion Based on Time-Frequency Optimization for P-Band Polarimetric SAR Interferometry

A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data

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