A Hierarchical Extended Multiple-Component Scattering Decomposition of Polarimetric SAR Interferometry

Wang, Yu; Yu, Weidong; Liu, Xiuqing; Wang, Chunle

doi:10.1109/lgrs.2019.2942090

Cited by 5 publications

(9 citation statements)

References 16 publications

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“…The unit projection vectors correspond to the polarization scattering system. The specific expression of the optimal parameter PISP is derived from [34].…”

Section: B Modification Of Polinsar Volume Scattering Modelsmentioning

confidence: 99%

“…According to [34], the PISP is independent of the absolute amplitude information of the scattering matrix and the orientation angle of the target and can be used to characterize the polarization interference stability information of various ground targets. As a second-order statistical parameter of PolInSAR, the PISP can be used to effectively describe the characteristics of ground scattering.…”

Section: B Modification Of Polinsar Volume Scattering Modelsmentioning

confidence: 99%

“…In the polarimetric SAR interferometry (PolInSAR) image data, target decomposition is one of the most effective applications [32]- [34]. PolInSAR, which combines interferometric and polarimetric information, not only successfully interprets the characteristics of the targets, but also distinguishes oriented buildings from the vegetation areas.…”

mentioning

confidence: 99%

“…PolInSAR, which combines interferometric and polarimetric information, not only successfully interprets the characteristics of the targets, but also distinguishes oriented buildings from the vegetation areas. Polarimetric interferometric similarity parameter (PISP) is an important parameter associated with the physical properties of various targets and is polarimetric dependent [33], [34]. The PISP magnitude in the forest areas is lower than that in the built-up areas that contain more permanent scatterers, and PolInSAR coherence has close relationship with the forest types and structures [35].…”

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confidence: 99%

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Five-Component Decomposition Methods of Polarimetric SAR and Polarimetric SAR Interferometry Using Coupling Scattering Mechanisms

Wang

Hou

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this article, five-component model-based decomposition methods of polarimetric synthetic aperture radar (PolSAR) (P5SD) and polarimetric synthetic aperture radar interferometry (PolInSAR) (PI5SD) using coupling scattering mechanisms are proposed. The target of these methods is to overcome the overestimation of volume scattering and to mitigate the mixed ambiguity of the scattering mechanism. First, coupling scattering models are proposed to decompose the coherency matrix into five submatrices. According to the traditional seven-component decomposition method proposed by Singh et al., the helix and mixed dipole can be merged into a coupling component, while the oriented dipole and compound dipole can also be generated in a general form. Then, the refined volume scattering models of PolSAR are introduced to characterize the volume scattering power of various land covers. The polarimetric interferometric similarity parameter (PISP) is introduced to modify the refined volume scattering models, and it can be used to further reduce the volume scattering contribution of building areas with large orientation angles. In addition, a feature named θ dv , total power, and PISP are embedded in the building area identification, and the building extraction results are used as the branch condition of the volume scattering model selection. In this article, the validity of P5SD and PI5SD can be demonstrated using airborne L-band E-SAR PolInSAR datasets and airborne C-band PolInSAR data collected by the Aerospace Information Research Institute, Chinese Academy of Sciences. The experimental results demonstrate that the P5SD and PI5SD can be used to effectively interpret the scattering characteristics of the ambiguous regions.

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“…The unit projection vectors correspond to the polarization scattering system. The specific expression of the optimal parameter PISP is derived from [34].…”

Section: B Modification Of Polinsar Volume Scattering Modelsmentioning

confidence: 99%

Section: B Modification Of Polinsar Volume Scattering Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Five-Component Decomposition Methods of Polarimetric SAR and Polarimetric SAR Interferometry Using Coupling Scattering Mechanisms

Wang

Hou

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In recent decades, by improving volume scattering models and introducing auxiliary mathematical tools, many methods with improved volume scattering models [19], [26]- [30] and extended scattering components [2], [31]- [33] have been proposed to overcome the OVS and negative scattering powers. Several models were compared in benchmarking exercises to evaluate the performance of the existing decomposition methods [29], [34].…”

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confidence: 99%

A Modified Four-Component Decomposition Method With Refined Volume Scattering Models

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this article, a modified four-component decomposition method with refined volume scattering models is proposed for polarimetric synthetic aperture radar (SAR) image processing. In the new decomposition method, after the orientation angle compensation, the orientation angle is placed in the probability density functions. General forms of the volume scattering models and branch conditions can be obtained. Similar to the four-component scattering power decomposition with extended volume scattering model (S4R) proposed by Sato et al., refined volume scattering models can be used for various land covers based on the criteria. Since the orientation angles are contained in the refined volume scattering models, the oriented buildings can be discriminated from the vegetation areas and the overestimation problem of volume scattering is substantially overcome. In this article, the performance of the proposed method is evaluated by the spaceborne C-band Gaofen-3 data and airborne L-band E-SAR data. Several approaches are employed as a comparison of the proposed methods. Experimental results show that, compared with the existing decomposition methods, the proposed method can effectively represent the scattering characteristics of the ambiguous regions, and the double-bounce scattering contributions over the urban areas can be substantially enhanced.

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Sparse vegetation height estimation based on non‐local sample selection with generalised inner product

Xu,

Cheng,

Xue

et al. 2024

IET Radar Sonar & Navi

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With the assumption of densely distributed vegetation in PolInSAR processing, the height parameters can be inversed by the conventional random volume over ground (RVoG) method. During the procedure of RVoG, the samples used to estimate the PolInSAR coherence are selected directly from the neighbouring areas. However, for sparsely distributed vegetation, the scattering statistics are different from those of densely distributed vegetation. Therefore, the inversion performance will be deteriorated if the samples are selected directly in the neighbouring areas. A new phase‐based method is proposed to select samples, whose positions represent the volume scattering pixels, for sparsely distributed vegetation height inversion. By analysing the scattering characteristics of sparsely distributed vegetation, the processing data vector is formulated based on the amplitude‐normalised interferograms. With the PolSAR classification, the generalised inner product (GIP) is iteratively used to select the non‐local samples based on the formulated phase data vectors, which are utilised for sparsely distributed vegetation height inversion. For the selected sample sets, the vegetation distribution can be approximately regarded as “dense distribution”, and then the vegetation height parameters can be inversed by RVoG method. Compared to the height inversion performance based on different sample selection methods, the effectiveness of the proposed method is validated by the PolSARPro simulated data and the real airborne L‐band PolInSAR data.

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A Hierarchical Extended Multiple-Component Scattering Decomposition of Polarimetric SAR Interferometry

Cited by 5 publications

References 16 publications

Five-Component Decomposition Methods of Polarimetric SAR and Polarimetric SAR Interferometry Using Coupling Scattering Mechanisms

Five-Component Decomposition Methods of Polarimetric SAR and Polarimetric SAR Interferometry Using Coupling Scattering Mechanisms

A Modified Four-Component Decomposition Method With Refined Volume Scattering Models

Sparse vegetation height estimation based on non‐local sample selection with generalised inner product

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