Quantitative Analysis of Polarimetric Model-Based Decomposition Methods
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 o...
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MDPI AG
2016-11-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/8/12/977 |
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| author | Qinghua Xie J. David Ballester-Berman Juan M. Lopez-Sanchez Jianjun Zhu Changcheng Wang |
| author_facet | Qinghua Xie J. David Ballester-Berman Juan M. Lopez-Sanchez Jianjun Zhu Changcheng Wang |
| author_sort | Qinghua Xie |
| collection | DOAJ |
| description | 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. |
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| id | doaj.art-b1ec2dc2da9e48c0b02af739d591fc47 |
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| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T03:00:10Z |
| publishDate | 2016-11-01 |
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| series | Remote Sensing |
| spelling | doaj.art-b1ec2dc2da9e48c0b02af739d591fc472022-12-21T17:18:14ZengMDPI AGRemote Sensing2072-42922016-11-0181297710.3390/rs8120977rs8120977Quantitative Analysis of Polarimetric Model-Based Decomposition MethodsQinghua Xie0J. David Ballester-Berman1Juan M. Lopez-Sanchez2Jianjun Zhu3Changcheng Wang4School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaInstitute for Computing Research (IUII), University of Alicante, Alicante E-03080, SpainInstitute for Computing Research (IUII), University of Alicante, Alicante E-03080, SpainSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaIn 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.http://www.mdpi.com/2072-4292/8/12/977model-based decompositionpolarimetric synthetic aperture radar (PolSAR)quantitative analysisMonte Carlo simulations |
| spellingShingle | Qinghua Xie J. David Ballester-Berman Juan M. Lopez-Sanchez Jianjun Zhu Changcheng Wang Quantitative Analysis of Polarimetric Model-Based Decomposition Methods Remote Sensing model-based decomposition polarimetric synthetic aperture radar (PolSAR) quantitative analysis Monte Carlo simulations |
| title | Quantitative Analysis of Polarimetric Model-Based Decomposition Methods |
| title_full | Quantitative Analysis of Polarimetric Model-Based Decomposition Methods |
| title_fullStr | Quantitative Analysis of Polarimetric Model-Based Decomposition Methods |
| title_full_unstemmed | Quantitative Analysis of Polarimetric Model-Based Decomposition Methods |
| title_short | Quantitative Analysis of Polarimetric Model-Based Decomposition Methods |
| title_sort | quantitative analysis of polarimetric model based decomposition methods |
| topic | model-based decomposition polarimetric synthetic aperture radar (PolSAR) quantitative analysis Monte Carlo simulations |
| url | http://www.mdpi.com/2072-4292/8/12/977 |
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