Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/154295
Title: Unsupervised Polarimetric SAR Image Classification Using \mathcal {G}_{p}^{0} Mixture Model
Authors: Juan I. Fern&x00E1;ndez-Michelli;Mart&x00ED;n Hurtado;Javier A. Areta;Carlos H. Muravchik
Year: 2017
Publisher: IEEE
Abstract: This letter proposes a polarimetric synthetic aperture radar image classification method based on the expectation-maximization algorithm. It is an unsupervised algorithm that determines the number of classes in the scene following a top-down strategy using a covariance-based hypothesis test. A G<sub>p</sub><sup>0</sup> mixture model is used to describe multilook complex polarimetric data, and the proposed algorithm is tested in simulated and real data sets obtaining good results. The classification performance is evaluated by means of the overall accuracy and the kappa indices obtained from the Monte Carlo analysis. Finally, the results are compared with those obtained by other classic and recently developed classification algorithms.
URI: http://localhost/handle/Hannan/154295
volume: 14
issue: 5
More Information: 754,
758
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7887730.pdf701.63 kBAdobe PDF
Title: Unsupervised Polarimetric SAR Image Classification Using \mathcal {G}_{p}^{0} Mixture Model
Authors: Juan I. Fern&x00E1;ndez-Michelli;Mart&x00ED;n Hurtado;Javier A. Areta;Carlos H. Muravchik
Year: 2017
Publisher: IEEE
Abstract: This letter proposes a polarimetric synthetic aperture radar image classification method based on the expectation-maximization algorithm. It is an unsupervised algorithm that determines the number of classes in the scene following a top-down strategy using a covariance-based hypothesis test. A G<sub>p</sub><sup>0</sup> mixture model is used to describe multilook complex polarimetric data, and the proposed algorithm is tested in simulated and real data sets obtaining good results. The classification performance is evaluated by means of the overall accuracy and the kappa indices obtained from the Monte Carlo analysis. Finally, the results are compared with those obtained by other classic and recently developed classification algorithms.
URI: http://localhost/handle/Hannan/154295
volume: 14
issue: 5
More Information: 754,
758
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7887730.pdf701.63 kBAdobe PDF
Title: Unsupervised Polarimetric SAR Image Classification Using \mathcal {G}_{p}^{0} Mixture Model
Authors: Juan I. Fern&x00E1;ndez-Michelli;Mart&x00ED;n Hurtado;Javier A. Areta;Carlos H. Muravchik
Year: 2017
Publisher: IEEE
Abstract: This letter proposes a polarimetric synthetic aperture radar image classification method based on the expectation-maximization algorithm. It is an unsupervised algorithm that determines the number of classes in the scene following a top-down strategy using a covariance-based hypothesis test. A G<sub>p</sub><sup>0</sup> mixture model is used to describe multilook complex polarimetric data, and the proposed algorithm is tested in simulated and real data sets obtaining good results. The classification performance is evaluated by means of the overall accuracy and the kappa indices obtained from the Monte Carlo analysis. Finally, the results are compared with those obtained by other classic and recently developed classification algorithms.
URI: http://localhost/handle/Hannan/154295
volume: 14
issue: 5
More Information: 754,
758
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7887730.pdf701.63 kBAdobe PDF