On the Use of Generalized Volume Scattering Models for the Improvement of General Polarimetric Model-Based Decomposition

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Title: On the Use of Generalized Volume Scattering Models for the Improvement of General Polarimetric Model-Based Decomposition
Authors: Xie, Qinghua | Ballester-Berman, J. David | Lopez-Sanchez, Juan M. | Zhu, Jianjun | Wang, Changcheng
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 | Universidad de Alicante. Instituto Universitario de Investigación Informática
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: 30-Jan-2017
Publisher: MDPI
Citation: Xie Q, Ballester-Berman JD, Lopez-Sanchez JM, Zhu J, Wang C. On the Use of Generalized Volume Scattering Models for the Improvement of General Polarimetric Model-Based Decomposition. Remote Sensing. 2017; 9(2):117. doi:10.3390/rs9020117
Abstract: Recently, a general polarimetric model-based decomposition framework was proposed by Chen et al., which addresses several well-known limitations in previous decomposition methods and implements a simultaneous full-parameter inversion by using complete polarimetric information. However, it only employs four typical models to characterize the volume scattering component, which limits the parameter inversion performance. To overcome this issue, this paper presents two general polarimetric model-based decomposition methods by incorporating the generalized volume scattering model (GVSM) or simplified adaptive volume scattering model, (SAVSM) proposed by Antropov et al. and Huang et al., respectively, into the general decomposition framework proposed by Chen et al. By doing so, the final volume coherency matrix structure is selected from a wide range of volume scattering models within a continuous interval according to the data itself without adding unknowns. Moreover, the new approaches rely on one nonlinear optimization stage instead of four as in the previous method proposed by Chen et al. In addition, the parameter inversion procedure adopts the modified algorithm proposed by Xie et al. which leads to higher accuracy and more physically reliable output parameters. A number of Monte Carlo simulations of polarimetric synthetic aperture radar (PolSAR) data are carried out and show that the proposed method with GVSM yields an overall improvement in the final accuracy of estimated parameters and outperforms both the version using SAVSM and the original approach. In addition, C-band Radarsat-2 and L-band AIRSAR fully polarimetric images over the San Francisco region are also used for testing purposes. A detailed comparison and analysis of decomposition results over different land-cover types are conducted. According to this study, the use of general decomposition models leads to a more accurate quantitative retrieval of target parameters. However, there exists a trade-off between parameter accuracy and model complexity which constrains the physical validity of solutions and must be further investigated.
Sponsor: This work was supported in part by National Nature Science Foundation of China under Grant 41531068, 41371335, 41671356 and 41274010, the Spanish Ministry of Economy and Competitiveness and EU FEDER under Project TIN2014-55413-C2-2-P, and China Scholarship Council under Grant 201406370079.
URI: http://hdl.handle.net/10045/62648
ISSN: 2072-4292
DOI: 10.3390/rs9020117
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: http://dx.doi.org/10.3390/rs9020117
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