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2020
Journal Article
Titel

Demonstration and analysis of an extended adaptive general four-component decomposition

Abstract
The overestimation of volume scattering is an essential shortcoming of the model-based polarimetric synthetic aperture radar (PolSAR) target decomposition method. It is likely to affect the measurement accuracy and result in mixed ambiguity of the scattering mechanism. In this article, an extended adaptive four-component decomposition method with multiple thresholds is proposed. First, the orientation angle compensation is applied to the coherency matrix, and artificial areas are extracted as the basis for selecting the decomposition method. Second, for the decomposition of artificial areas, one of the two complex unitary transformation matrices of the coherency matrix is selected according to the wave anisotropy (Aw). In addition, the branch condition that is used as a criterion for the hierarchical implementation decomposition is the ratio of the correlation coefficient (Rcc). Finally, the selected unitary transformation matrix and the discriminative threshold are use d to determine the structure of the selected volume scattering models, which are more effective to adapt to various scattering mechanisms. In this article, the performance of the proposed method is evaluated on GaoFen-3 full PolSAR datasets for various time periods and regions. The experimental results demonstrate that the proposed method can effectively represent the scattering characteristics of the ambiguous regions, and the oriented building areas can be well discriminated as dihedral or odd-bounce structures.
Author(s)
Wang, Yu
Chinese Academy of Sciences, Beijing, China
Yu, Weidong
Chinese Academy of Sciences, Beijing, China
Liu, Xiuqing
Chinese Academy of Sciences, Beijing, China
Wang, Chunle
Chinese Academy of Sciences, Beijing, China
Kuijper, Arjan
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Guthe, Stefan
Fraunhofer-Institut für Graphische Datenverarbeitung IGD
Zeitschrift
IEEE journal of selected topics in applied earth observations and remote sensing
Funder
National Science Foundation NSF
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DOI
10.1109/JSTARS.2020.2996801
Externer Link
Externer Link
Language
English
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Fraunhofer-Institut für Graphische Datenverarbeitung IGD
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  • Lead Topic: Smart Cit...

  • Research Line: Comput...

  • light scattering

  • imaging technology co...

  • satellite data

  • satellite image

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