Estimation of Forest Structure, Ground, and Canopy Layer Characteristics From Multibaseline Polarimetric Interferometric SAR Data

Neumann, Maxim; Ferro-Famil, Laurent; Reigber, Andreas

doi:10.1109/tgrs.2009.2031101

Cited by 229 publications

(154 citation statements)

References 36 publications

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“…defined in a continuous range of parameters) instead of just four discrete models. All available generalized volume models [10]- [15] are defined by one or more parameters. Consequently, this alternative entails increasing the total number of model parameters (unknowns to be estimated) above the number of observations, hence the inversion becomes an underdetermined problem.…”

Section: Introductionmentioning

confidence: 99%

“…considering also 22 33 T T  ) needed further investigation. In this letter, we propose a general four-component model-based decomposition method which employs a simplified generalized volume scattering model, the original version of which was proposed by Neumann et al [14], [15]. The adopted volume scattering model can cover cases with both 22= 33 T T and 22 33 T T  , which are classified by a randomness parameter.…”

Section: Introductionmentioning

confidence: 99%

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A Modified General Polarimetric Model-Based Decomposition Method With the Simplified Neumann Volume Scattering Model

Xie

Zhu

López-Sánchez

et al. 2018

IEEE Geosci. Remote Sensing Lett.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Modified General Polarimetric Model-Based Decomposition Method With the Simplified Neumann Volume Scattering Model

Xie

Zhu

López-Sánchez

et al. 2018

IEEE Geosci. Remote Sensing Lett.

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“…Moreover, vegetation height is an essential factor for the estimation of the biomass stored in vegetation [3,4]. Therefore, accurately extracting vegetation height at a large scale is an important task.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, accurately extracting vegetation height at a large scale is an important task. Given the fact that polarimetric interferometric synthetic aperture radar (PolInSAR) can separate the scattering power of a single resolution cell into the contributions of surface, double-bounce, and volume scattering, it can be considered to be a viable remote sensing technique for estimating vegetation height in large-scale areas [4][5][6][7][8][9].…”

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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“…The recently development new radar techniques, Polarimetric and Interferometric SAR (POLINSAR), give the multitude observers to describe the physical and geometry characteristics which used in digital elevation model (DEM) estimation, vegetation structure estimation and the target recognition, .etc (Saatchi, S., 1997;Neumann, M., 2010;Mette, T.,2004).…”

Section: Introductionmentioning

confidence: 99%

The Low Backscattering Targets Classification in Urban Areas

Shi

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The Polarimetric and Interferometric Synthetic Aperture Radar (POLINSAR) is widely used in urban area nowadays. Because of the physical and geometric sensitivity, the POLINSAR is suitable for the city classification, power-lines detection, building extraction, etc. As the new X-band POLINSAR radar, the china prototype airborne system, XSAR works with high spatial resolution in azimuth (0.1m) and slant range (0.4m). In land applications, SAR image classification is a useful tool to distinguish the interesting area and obtain the target information. The bare soil, the cement road, the water and the building shadow are common scenes in the urban area. As it always exists low backscattering sign objects (LBO) with the similar scattering mechanism (all odd bounce except for shadow) in the XSAR images, classes are usually confused in Wishart-H-Alpha and Freeman-Durden methods. It is very hard to distinguish those targets only using the general information. To overcome the shortage, this paper explores an improved algorithm for LBO refined classification based on the Pre-Classification in urban areas. Firstly, the PreClassification is applied in the polarimetric datum and the mixture class is marked which contains LBO. Then, the polarimetric covariance matrix C3 is re-estimated on the Pre-Classification results to get more reliable results. Finally, the occurrence space which combining the entropy and the phase-diff standard deviation between HH and VV channel is used to refine the PreClassification results. The XSAR airborne experiments show the improved method is potential to distinguish the mixture classes in the low backscattering objects.

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Estimation of Forest Structure, Ground, and Canopy Layer Characteristics From Multibaseline Polarimetric Interferometric SAR Data

Abstract: WOSInternational audienc

Cited by 229 publications

References 36 publications

A Modified General Polarimetric Model-Based Decomposition Method With the Simplified Neumann Volume Scattering Model

A Modified General Polarimetric Model-Based Decomposition Method With the Simplified Neumann Volume Scattering Model

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

The Low Backscattering Targets Classification in Urban Areas

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