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Demonstration and Analysis of an Extended Adaptive General Four-Component Decomposition

Wang, Yu ; Yu, Weidong ; Liu, Xiuqing ; Wang, Chunle ; Kuijper, Arjan ; Guthe, Stefan (2020)
Demonstration and Analysis of an Extended Adaptive General Four-Component Decomposition.
In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13
doi: 10.1109/JSTARS.2020.2996801
Article, Bibliographie

Abstract

The overestimation of volume scattering is an essentialshortcoming of the model-based polarimetric syntheticaperture radar (PolSAR) target decomposition method. It islikely to affect the measurement accuracy and result in mixedambiguity of scattering mechanism. In this paper, an extendedadaptive four-component decomposition method (ExAG4UThs)is proposed. First, the orientation angle compensation (OAC)is applied to the coherency matrix and artificial areas areextracted as the basis for selecting the decomposition method.Second, for the decomposition of artificial areas, one of the twocomplex unitary transformation matrices of the coherency matrixis selected according to the wave anisotropy (Aw). In addition, thebranch condition that is used as a criterion for the hierarchicalimplementation decomposition is the ratio of the correlationcoefficient (Rcc). Finally, the selected unitary transformationmatrix and discriminative threshold are used to determine thestructure of the selected volume scattering models, which aremore effectively to adapt to various scattering mechanisms. Inthis paper, the performance of the proposed method is evaluatedon GaoFen-3 full PolSAR data sets for various time periods andregions. The experimental results demonstrate that the proposedmethod can effectively represent the scattering characteristics ofthe ambiguous regions and the oriented building areas can bewell discriminated as dihedral or odd-bounce structures.

Item Type: Article
Erschienen: 2020
Creators: Wang, Yu ; Yu, Weidong ; Liu, Xiuqing ; Wang, Chunle ; Kuijper, Arjan ; Guthe, Stefan
Type of entry: Bibliographie
Title: Demonstration and Analysis of an Extended Adaptive General Four-Component Decomposition
Language: English
Date: 28 May 2020
Publisher: IEEE
Journal or Publication Title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume of the journal: 13
DOI: 10.1109/JSTARS.2020.2996801
Abstract:

The overestimation of volume scattering is an essentialshortcoming of the model-based polarimetric syntheticaperture radar (PolSAR) target decomposition method. It islikely to affect the measurement accuracy and result in mixedambiguity of scattering mechanism. In this paper, an extendedadaptive four-component decomposition method (ExAG4UThs)is proposed. First, the orientation angle compensation (OAC)is applied to the coherency matrix and artificial areas areextracted as the basis for selecting the decomposition method.Second, for the decomposition of artificial areas, one of the twocomplex unitary transformation matrices of the coherency matrixis selected according to the wave anisotropy (Aw). In addition, thebranch condition that is used as a criterion for the hierarchicalimplementation decomposition is the ratio of the correlationcoefficient (Rcc). Finally, the selected unitary transformationmatrix and discriminative threshold are used to determine thestructure of the selected volume scattering models, which aremore effectively to adapt to various scattering mechanisms. Inthis paper, the performance of the proposed method is evaluatedon GaoFen-3 full PolSAR data sets for various time periods andregions. The experimental results demonstrate that the proposedmethod can effectively represent the scattering characteristics ofthe ambiguous regions and the oriented building areas can bewell discriminated as dihedral or odd-bounce structures.

Uncontrolled Keywords: Light scattering, Imaging technology concepts, Satellite data, Satellite images
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 19 Oct 2020 08:29
Last Modified: 09 Dec 2021 09:49
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