Eigenvalue/eigenvector‐based serial decomposition of the polarimetric synthetic aperture radar coherency matrix

Zhu, Feiya; Zhang, Yunhua; Liu, Dong

doi:10.1049/iet-rsn.2017.0158

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…And they can make PolSAR data used more widely, such as vegetation classification [2], soil moisture estimation [3], marine monitoring [4], building information extraction [5], and ship detection [6], etc. Currently, polarimetric target decomposition methods are mainly divided into two categories: decomposition based on eigenvalues and eigenvectors [7]- [9], and decomposition based on physical models [10]- [26]. The latter has become one of the most mainstream methods of incoherent decomposition because they display the scattering model of different ground targets in a straightforward way.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Polarimetric Target Decomposition With Power Redistribution for Urban Areas

Hu,

Wang,

Sun

et al. 2023

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

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Polarimetric decomposition of oriented buildings is challenging due to their variable orientation angles and structures. Both vegetated and oriented built-up areas generate the HV component namely cross-polarized scattering, leading to an overestimation of volume scattering (OVS). It can cause misinterpretation of the scattering mechanisms between oriented built-up and vegetated areas. In this article, we use a pure volume scattering model designed for completely random scattering to describe the scattering of vegetated areas. At the same time, a new urban revised rate is proposed by considering the rotated dihedral model and introducing polarimetric asymmetry (PA), which can distinguish different areas and reduce OVS in urban areas through power transfer strategy. Then a new model-based polarimetric target decomposition with power redistribution for urban areas is proposed. The performance of our proposed method is verified by RADARSAT-2 C-band and UAVSAR Lband data. The results show that our method can not only better characterize the scattering of oriented buildings compared to previous methods, but also maintain the scattering power of natural areas. It can alleviate the scattering misinterpretation problem between oriented built-up and natural areas.

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Section: Introductionmentioning

confidence: 99%

Model-Based Polarimetric Target Decomposition With Power Redistribution for Urban Areas

Hu,

Wang,

Sun

et al. 2023

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

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“…Then, some excellent publications have appeared in the literature [8]- [14], and the review of these decomposition theorems is summarized in [15]. The decomposition technique is divided into two main categories: Model-based decomposition [16]- [22] and eigenvalue-eigenvector-based decomposition [23]- [25]. Among them, the model-based decomposition is directly related to the physical scattering mechanism, which can be constructed based on coherency or covariance matrix.…”

mentioning

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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Unsupervised classification using polarimetric interferometric decomposition and Wishart classifier

Wang

2019

International Journal of Remote Sensing

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Eigenvalue/eigenvector‐based serial decomposition of the polarimetric synthetic aperture radar coherency matrix

Cited by 6 publications

References 30 publications

Model-Based Polarimetric Target Decomposition With Power Redistribution for Urban Areas

Model-Based Polarimetric Target Decomposition With Power Redistribution for Urban Areas

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

Unsupervised classification using polarimetric interferometric decomposition and Wishart classifier

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