SAR image segmentation using level sets and region competition under the GH model

Synthetic Aperture Radar (SAR) images are dificult to segment due to their characteristic noise, called speckle, which is multiplicative, non-gaussian and has a low signal to noise ratio. In this work we use the GH distribution to model the SAR data from the different regions of the image. We estima...

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Detalles Bibliográficos
Autores principales: Buemi, M.E., Goussies, N., Jacobo, J., Mejail, M.
Formato: Artículo publishedVersion
Publicado: 2009
Materias:
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_03029743_v5856LNCS_n_p153_Buemi
Aporte de:Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires Ver origen
Descripción
Sumario:Synthetic Aperture Radar (SAR) images are dificult to segment due to their characteristic noise, called speckle, which is multiplicative, non-gaussian and has a low signal to noise ratio. In this work we use the GH distribution to model the SAR data from the different regions of the image. We estimate their statistical parameters and use them in a segmentation algorithm based on multiregion competition. We then apply this algorithm to segment simulated as well as real SAR images and evaluate the accuracy of the segmentation results obtained. © 2009 Springer-Verlag Berlin Heidelberg.