Fitting a Two-Component Scattering Model to Polarimetric SAR Data From Forests

Freeman, A.

doi:10.1109/tgrs.2007.897929

Cited by 194 publications

(123 citation statements)

References 20 publications

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“…It is worth noting that the adaptive volume scattering model proposed in this paper is similar to that used by Freeman [11], which can be rewritten in its equivalent coherency matrix form as: (21) By comparing Equations (12) and (21), the difference lies in the range for . However, Equation (21) cannot be directly used in the three-component decomposition scheme because there exists risk in over-estimating the volume scattering power (note can be arbitrarily large by definition).…”

Section: Discussionmentioning

confidence: 99%

“…The volume scattering is originally modeled by a cloud of uniformly distributed dipoles [1] whose coherency matrix is given by: (11) However, it has been observed that this model sometimes leads to over-estimation of the volume scattering power and consequently generates a negative power in the derived surface and double-bounce scattering [3]. This phenomenon happens whenever in the transformed coherency matrix.…”

Section: Adaptive Volume Scattering Model and Power Decompositionmentioning

confidence: 99%

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Three-Component Power Decomposition for Polarimetric SAR Data Based on Adaptive Volume Scatter Modeling

et al. 2012

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Abstract:In this paper, the three-component power decomposition for polarimetric SAR (PolSAR) data with an adaptive volume scattering model is proposed. The volume scattering model is assumed to be reflection-symmetric but parameterized. For each image pixel, the decomposition first starts with determining the adaptive parameter based on matrix similarity metric. Then, a respective scattering power component is retrieved with the established procedure. It has been shown that the proposed method leads to complete elimination of negative powers as the result of the adaptive volume scattering model. Experiments with the PolSAR data from both the NASA/JPL (National Aeronautics and Space Administration/Jet Propulsion Laboratory) Airborne SAR (AIRSAR) and the JAXA (Japan Aerospace Exploration Agency) ALOS-PALSAR also demonstrate that the proposed method not only obtains similar/better results in vegetated areas as compared to the existing Freeman-Durden decomposition but helps to improve discrimination of the urban regions.

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Section: Discussionmentioning

confidence: 99%

Section: Adaptive Volume Scattering Model and Power Decompositionmentioning

confidence: 99%

Three-Component Power Decomposition for Polarimetric SAR Data Based on Adaptive Volume Scatter Modeling

et al. 2012

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“…This approach represents target scattering by several basic scattering mechanisms. Since 1970, this technique has become an advanced research area in polarimetric SAR signal processing, with many valuable coherent and incoherent decompositions being developed [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Among these methods, Cloude-Pottier decomposition has attracted considerable attention.…”

Section: Introductionmentioning

confidence: 99%

Scattering Mechanism Extraction by a Modified Cloude-Pottier Decomposition for Dual Polarization SAR

Wu²

2015

Remote Sensing

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Dual polarization is a typical operational mode of polarimetric synthetic aperture radar (SAR). However, few studies have considered the scattering mechanism extraction of dual-polarization SARs. A modified Cloude-Pottier decomposition is proposed to investigate the performance of the scattering mechanism extraction of dual-polarization SARs. It is theoretically demonstrated that only HH-VV SAR can discriminate the three canonical scattering mechanisms from an isotropic surface, horizontal dipole, and isotropic dihedral. Various experiments are conducted using 21 scenes from real datasets acquired by AIRSAR, Convair-580 SAR, EMISAR, E-SAR, Pi-SAR, and RADARSAT-2. Division of the dual-polarization H-α plane is experimentally obtained. The lack of cross-polarization induces the diffusion of scattering mechanisms and their overlap in the HH-VV H-α plane. However, the performance of HH-VV SAR for extracting scattering mechanisms is acceptable. Thus, HH-VV SAR is a suitable alternative to full-polarization SAR in certain cases. Meanwhile, the extraction performance of the other two dual-polarization SARs is badly degraded due to the lack of co-polarization. Therefore, HH-HV and HV-VV SARs cannot effectively extract the scattering mechanisms in the H-α plane.

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“…This entails the existence of a polarization where volume scattering does not contribute, after which it follows that this solution is not consistent with physical model for forest scattering (44), (38). The ground structure matrix is characterized by the lowest coherence values, being contaminated by volume contributions.…”

Section: Physical Interpretationmentioning

confidence: 99%

“…: the ground and the vegetation layer. This idea has been formalized in literature through different physical models, considering the features of ground and volume scattering in polarimetric data (38), single-polarization tomographic data, as shown in (26) and in the previous section, or in polarimetric and interferometric (PolInSAR) data (13), (39), (27). Beside physical soundness, however, the popularity of two-layered models is also due to the fact that they provide a sufficiently simple mathematical framework to allow model inversion.…”

Section: Introductionmentioning

confidence: 99%

Forest Structure Retrieval from Multi-Baseline SARs

Tebaldini¹

2012

Remote Sensing of Biomass - Principles and Applications

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Fitting a Two-Component Scattering Model to Polarimetric SAR Data From Forests

Cited by 194 publications

References 20 publications

Three-Component Power Decomposition for Polarimetric SAR Data Based on Adaptive Volume Scatter Modeling

Three-Component Power Decomposition for Polarimetric SAR Data Based on Adaptive Volume Scatter Modeling

Scattering Mechanism Extraction by a Modified Cloude-Pottier Decomposition for Dual Polarization SAR

Forest Structure Retrieval from Multi-Baseline SARs

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