SAR Image Segmentation Using Level Sets and Region Competition under the $\mathcal{G}^H$ Model

Authors:
Maria Elena Buemi;Norberto Goussies;Julio Jacobo;Marta Mejail
Affiliations:
Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad de Buenos Aires, República Argentina 1428;Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad de Buenos Aires, República Argentina 1428;Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad de Buenos Aires, República Argentina 1428;Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad de Buenos Aires, República Argentina 1428
Venue:
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2009

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Abstract

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 $\mathcal{G}^{H}$ 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.