Quantitative Analysis of Polarimetric Model-Based Decomposition Methods

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/60327
Información del item - Informació de l'item - Item information
Title: Quantitative Analysis of Polarimetric Model-Based Decomposition Methods
Authors: Xie, Qinghua | Ballester Berman, Josep 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 | Polarimetric synthetic aperture radar (PolSAR) | Quantitative analysis | Monte Carlo simulations
Knowledge Area: Teoría de la Señal y Comunicaciones
Issue Date: 25-Nov-2016
Publisher: MDPI
Citation: Xie Q, Ballester-Berman JD, Lopez-Sanchez JM, Zhu J, Wang C. Quantitative Analysis of Polarimetric Model-Based Decomposition Methods. Remote Sensing. 2016; 8(12):977. doi:10.3390/rs8120977
Abstract: In this paper, we analyze the robustness of the parameter inversion provided by general polarimetric model-based decomposition methods from the perspective of a quantitative application. The general model and algorithm we have studied is the method proposed recently by Chen et al., which makes use of the complete polarimetric information and outperforms traditional decomposition methods in terms of feature extraction from land covers. Nevertheless, a quantitative analysis on the retrieved parameters from that approach suggests that further investigations are required in order to fully confirm the links between a physically-based model (i.e., approaches derived from the Freeman–Durden concept) and its outputs as intermediate products before any biophysical parameter retrieval is addressed. To this aim, we propose some modifications on the optimization algorithm employed for model inversion, including redefined boundary conditions, transformation of variables, and a different strategy for values initialization. A number of Monte Carlo simulation tests for typical scenarios are carried out and show that the parameter estimation accuracy of the proposed method is significantly increased with respect to the original implementation. Fully polarimetric airborne datasets at L-band acquired by German Aerospace Center’s (DLR’s) experimental synthetic aperture radar (E-SAR) system were also used for testing purposes. The results show different qualitative descriptions of the same cover from six different model-based methods. According to the Bragg coefficient ratio (i.e., β ), they are prone to provide wrong numerical inversion results, which could prevent any subsequent quantitative characterization of specific areas in the scene. Besides the particular improvements proposed over an existing polarimetric inversion method, this paper is aimed at pointing out the necessity of checking quantitatively the accuracy of model-based PolSAR techniques for a reliable physical description of land covers beyond their proven utility for qualitative features extraction.
Sponsor: This work was supported by National Natural Science Foundation of China (No. 41531068, 41371335, and 41274010), the Spanish Ministry of Economy and Competitiveness and EU FEDER under Projects TEC2011-28201-C02-02 and TIN2014-55413-C2-2-P, and China Scholarship Council (No.201406370079).
URI: http://hdl.handle.net/10045/60327
ISSN: 2072-4292
DOI: 10.3390/rs8120977
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2016 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/rs8120977
Appears in Collections:INV - SST - Artículos de Revistas

Files in This Item:
Files in This Item:
File Description SizeFormat 
Thumbnail2016_Xie_etal_RemoteSens.pdf3,45 MBAdobe PDFOpen Preview

Items in RUA are protected by copyright, with all rights reserved, unless otherwise indicated.