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dc.contributor.author
Fernández Michelli, Juan Ignacio  
dc.contributor.author
Hurtado, Martin  
dc.contributor.author
Areta, Javier Alberto  
dc.contributor.author
Muravchik, Carlos Horacio  
dc.date.available
2019-03-22T19:55:29Z  
dc.date.issued
2017-05  
dc.identifier.citation
Fernández Michelli, Juan Ignacio; Hurtado, Martin; Areta, Javier Alberto; Muravchik, Carlos Horacio; Unsupervised Polarimetric SAR Image Classification Using Gp 0 Mixture Model; Institute of Electrical and Electronics Engineers; Ieee Geoscience and Remote Sensing Letters; 14; 5; 5-2017; 754-758  
dc.identifier.issn
1545-598X  
dc.identifier.uri
http://hdl.handle.net/11336/72339  
dc.description.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 G0 p 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.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Institute of Electrical and Electronics Engineers  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
Classification  
dc.subject
Expectation-Maximization (Em) Algorithm  
dc.subject
G0 P Distribution  
dc.subject
Mixture Models  
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Radar Signal Processing  
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Synthetic Aperture Radar (Sar) Images  
dc.subject.classification
Ingeniería de Sistemas y Comunicaciones  
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información  
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS  
dc.title
Unsupervised Polarimetric SAR Image Classification Using Gp 0 Mixture Model  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2019-03-15T18:25:30Z  
dc.journal.volume
14  
dc.journal.number
5  
dc.journal.pagination
754-758  
dc.journal.pais
Estados Unidos  
dc.description.fil
Fil: Fernández Michelli, Juan Ignacio. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.description.fil
Fil: Hurtado, Martin. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.description.fil
Fil: Areta, Javier Alberto. Universidad Nacional de Rio Negro. Sede Andina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.description.fil
Fil: Muravchik, Carlos Horacio. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.journal.title
Ieee Geoscience and Remote Sensing Letters  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/LGRS.2017.2679103  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/7887730