A Modified General Polarimetric Model-Based Decomposition Method With the Simplified Neumann Volume Scattering Model

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Title: A Modified General Polarimetric Model-Based Decomposition Method With the Simplified Neumann Volume Scattering Model
Authors: Xie, Qinghua | Zhu, Jianjun | Lopez-Sanchez, Juan M. | Wang, Changcheng | Fu, Haiqiang
Research Group/s: Señales, Sistemas y Telecomunicación
Center, Department or Service: Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal
Keywords: Model-based decomposition | Generalized volume scattering model | Synthetic aperture radar (SAR) | Monte Carlo simulation | Radar polarimetry
Knowledge Area: Teoría de la Señal y Comunicaciones
Issue Date: 17-May-2018
Publisher: IEEE
Citation: IEEE Geoscience and Remote Sensing Letters. 2018, 15(8): 1229-1233. doi:10.1109/LGRS.2018.2830503
Abstract: This letter proposes a modified general polarimetric model-based decomposition method which includes a simplified Neumann volume scattering model (SNVSM). This is useful to avoid a known limitation in one of the state-of-the-art general model-based decomposition methods (i.e., Chen's method), which considers only four possible discrete volume scattering models. Two types of SNVSM, assuming horizontal or vertical dipoles, are derived from the Neumann volume scattering model. The resulting volume coherency matrix exhibits a continuous range of volume scattering models. In addition, this volume model covers both random and nonrandom volume cases, which are distinguished by a randomness parameter. Monte Carlo simulations are used to test this approach. The proposed method with SNVSM overall improves the final accuracy of estimated parameters in comparison with the original approach and shows consistency with another existing generalized volume scattering model (GVSM). In addition, results from two fully polarimetric C- and L-band AIRSAR images over San Francisco region show that the proposed method produces reasonably physical results and outperforms the traditional Y4R method. Finally, the differences obtained between SNVSM and GVSM in two building areas show the potential advantage of SNVSM in identifying more types of volume scenes than that of GVSM.
Sponsor: This work was supported in part by National Natural Science Foundation of China under Grant 41531068, 41371335 and 41274010, Spanish MINECO, AEI and EU ERDF under Projects TIN2014-55413-C2-2-P and TEC2017-85244-C2-1-P, and China Scholarship Council under Grant 201406370079.
URI: http://hdl.handle.net/10045/76447
ISSN: 1545-598X (Print) | 1558-0571 (Online)
DOI: 10.1109/LGRS.2018.2830503
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 IEEE
Peer Review: si
Publisher version: https://doi.org/10.1109/LGRS.2018.2830503
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