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Unsupervised classification of multilook polarimetric SAR data using spatially variant wishart mixture model with double constraints

Chi Liu (UGent) , Wenzhi Liao (UGent) , Heng-Chao Li, Kun Fu and Wilfried Philips (UGent)
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Abstract
This paper addresses the unsupervised classification problems for multilook Polarimetric synthetic aperture radar (PolSAR) images by proposing a patch-level spatially variant Wishart mixture model (SVWMM) with double constraints. We construct this model by jointly modeling the pixels in a patch (rather than an individual pixel) so as to effectively capture the local correlation in the PolSAR images. More importantly, a responsibility parameter is introduced to the proposed model, providing not only the possibility to represent the importance of different pixels within a patch but also the additional flexibility for incorporating the spatial information. As such, double constraints are further imposed by simultaneously utilizing the similarities of the neighboring pixels, respectively, defined on two different parameter spaces (i.e., the hyperparameter in the posterior distribution of mixing coefficients and the responsibility parameter). Furthermore, the variational inference algorithm is developed to achieve effective learning of the proposed SVWMM with the closed-form updates, facilitating the automatic determination of the cluster number. Experimental results on several PolSAR data sets from both airborne and spaceborne sensors demonstrate that the proposed method is effective and it enables better performances on unsupervised classification than the conventional methods.

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Chicago
Liu, Chi, Wenzhi Liao, Heng-Chao Li, Kun Fu, and Wilfried Philips. 2018. “Unsupervised Classification of Multilook Polarimetric SAR Data Using Spatially Variant Wishart Mixture Model with Double Constraints.” Ieee Transactions on Geoscience and Remote Sensing 56 (10): 5600–5613.
APA
Liu, Chi, Liao, W., Li, H.-C., Fu, K., & Philips, W. (2018). Unsupervised classification of multilook polarimetric SAR data using spatially variant wishart mixture model with double constraints. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 56(10), 5600–5613.
Vancouver
1.
Liu C, Liao W, Li H-C, Fu K, Philips W. Unsupervised classification of multilook polarimetric SAR data using spatially variant wishart mixture model with double constraints. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING . IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS; 2018;56(10):5600–13.
MLA
Liu, Chi et al. “Unsupervised Classification of Multilook Polarimetric SAR Data Using Spatially Variant Wishart Mixture Model with Double Constraints.” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 56.10 (2018): 5600–5613. Print.
@article{8559413,
  abstract     = {This paper addresses the unsupervised classification problems for multilook Polarimetric synthetic aperture radar (PolSAR) images by proposing a patch-level spatially variant Wishart mixture model (SVWMM) with double constraints. We construct this model by jointly modeling the pixels in a patch (rather than an individual pixel) so as to effectively capture the local correlation in the PolSAR images. More importantly, a responsibility parameter is introduced to the proposed model, providing not only the possibility to represent the importance of different pixels within a patch but also the additional flexibility for incorporating the spatial information. As such, double constraints are further imposed by simultaneously utilizing the similarities of the neighboring pixels, respectively, defined on two different parameter spaces (i.e., the hyperparameter in the posterior distribution of mixing coefficients and the responsibility parameter). Furthermore, the variational inference algorithm is developed to achieve effective learning of the proposed SVWMM with the closed-form updates, facilitating the automatic determination of the cluster number. Experimental results on several PolSAR data sets from both airborne and spaceborne sensors demonstrate that the proposed method is effective and it enables better performances on unsupervised classification than the conventional methods.},
  author       = {Liu, Chi and Liao, Wenzhi and Li, Heng-Chao and Fu, Kun and Philips, Wilfried},
  issn         = {0196-2892},
  journal      = {IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING },
  language     = {eng},
  number       = {10},
  pages        = {5600--5613},
  publisher    = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS},
  title        = {Unsupervised classification of multilook polarimetric SAR data using spatially variant wishart mixture model with double constraints},
  url          = {http://dx.doi.org/10.1109/tgrs.2018.2819995},
  volume       = {56},
  year         = {2018},
}

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