Fisher Vectors for PolSAR Image Classification
- Autores
- Redolfi, Javier A.; Sánchez, Jorge; Flesia, Ana Georgina
- Año de publicación
- 2018
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- In this letter we study the application of the Fisher Vector (FV) to the problem of pixel-wise supervised classification of PolSAR images. This is a challenging problem since information in those images is encoded as complex-valued covariance matrices. We observe that the real part of these matrices preserve the positive semidefiniteness property of their complex counterpart. Based on this observation, we derive a FV from a mixture of real Wishart pdfs and integrate it with a Potts-like energy model in order to capture spatial dependencies between neighboring regions. Experimental results on two challenging datasets show the effectiveness of the approach.
Sociedad Argentina de Informática e Investigación Operativa - Materia
-
Ciencias Informáticas
PolSAR
fisher vectors
image classification - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-sa/3.0/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/71074
Ver los metadatos del registro completo
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Fisher Vectors for PolSAR Image ClassificationRedolfi, Javier A.Sánchez, JorgeFlesia, Ana GeorginaCiencias InformáticasPolSARfisher vectorsimage classificationIn this letter we study the application of the Fisher Vector (FV) to the problem of pixel-wise supervised classification of PolSAR images. This is a challenging problem since information in those images is encoded as complex-valued covariance matrices. We observe that the real part of these matrices preserve the positive semidefiniteness property of their complex counterpart. Based on this observation, we derive a FV from a mixture of real Wishart pdfs and integrate it with a Potts-like energy model in order to capture spatial dependencies between neighboring regions. Experimental results on two challenging datasets show the effectiveness of the approach.Sociedad Argentina de Informática e Investigación Operativa2018-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionResumenhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/71074enginfo:eu-repo/semantics/altIdentifier/url/http://47jaiio.sadio.org.ar/sites/default/files/CAI-14.pdfinfo:eu-repo/semantics/altIdentifier/issn/2525-0949info:eu-repo/semantics/reference/doi/10.1109/LGRS.2017.2750800info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/3.0/Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:43:30Zoai:sedici.unlp.edu.ar:10915/71074Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:43:30.251SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
Fisher Vectors for PolSAR Image Classification |
| title |
Fisher Vectors for PolSAR Image Classification |
| spellingShingle |
Fisher Vectors for PolSAR Image Classification Redolfi, Javier A. Ciencias Informáticas PolSAR fisher vectors image classification |
| title_short |
Fisher Vectors for PolSAR Image Classification |
| title_full |
Fisher Vectors for PolSAR Image Classification |
| title_fullStr |
Fisher Vectors for PolSAR Image Classification |
| title_full_unstemmed |
Fisher Vectors for PolSAR Image Classification |
| title_sort |
Fisher Vectors for PolSAR Image Classification |
| dc.creator.none.fl_str_mv |
Redolfi, Javier A. Sánchez, Jorge Flesia, Ana Georgina |
| author |
Redolfi, Javier A. |
| author_facet |
Redolfi, Javier A. Sánchez, Jorge Flesia, Ana Georgina |
| author_role |
author |
| author2 |
Sánchez, Jorge Flesia, Ana Georgina |
| author2_role |
author author |
| dc.subject.none.fl_str_mv |
Ciencias Informáticas PolSAR fisher vectors image classification |
| topic |
Ciencias Informáticas PolSAR fisher vectors image classification |
| dc.description.none.fl_txt_mv |
In this letter we study the application of the Fisher Vector (FV) to the problem of pixel-wise supervised classification of PolSAR images. This is a challenging problem since information in those images is encoded as complex-valued covariance matrices. We observe that the real part of these matrices preserve the positive semidefiniteness property of their complex counterpart. Based on this observation, we derive a FV from a mixture of real Wishart pdfs and integrate it with a Potts-like energy model in order to capture spatial dependencies between neighboring regions. Experimental results on two challenging datasets show the effectiveness of the approach. Sociedad Argentina de Informática e Investigación Operativa |
| description |
In this letter we study the application of the Fisher Vector (FV) to the problem of pixel-wise supervised classification of PolSAR images. This is a challenging problem since information in those images is encoded as complex-valued covariance matrices. We observe that the real part of these matrices preserve the positive semidefiniteness property of their complex counterpart. Based on this observation, we derive a FV from a mixture of real Wishart pdfs and integrate it with a Potts-like energy model in order to capture spatial dependencies between neighboring regions. Experimental results on two challenging datasets show the effectiveness of the approach. |
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2018 |
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2018-09 |
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eng |
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