Classification of water regions in SAR images using level sets and non-parametric density estimation

This paper presents a semi-supervised algorithm for the classification of water regions in SAR images. The proposed technique is based on region based level sets and non-parametric estimation of the probability density function (PDF) of the pixel intensities. The level set framework allows automatic...

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Bibliographic Details
Published in2009 16th IEEE International Conference on Image Processing (ICIP) pp. 1685 - 1688
Main Authors Silveira, Margarida, Heleno, Sandra
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Clouds
Floods
Image segmentation
Level set
level set methods
non-parametric density estimation
Radar imaging
Radar scattering
Rivers
Spaceborne radar
Synthetic aperture radar
Topology
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Summary:This paper presents a semi-supervised algorithm for the classification of water regions in SAR images. The proposed technique is based on region based level sets and non-parametric estimation of the probability density function (PDF) of the pixel intensities. The level set framework allows automatic topology adaptation and provides the regularization while the PDF's are estimated in each region using Parzen windows. Using non-parametric density estimation gives the method the flexibility to be used with different kinds of SAR data. To illustrate the performance of the proposed algorithm, the method is applied to the problems of river mapping and coastline extraction in real amplitude SAR images.
ISBN:9781424456536
1424456533
ISSN:1522-4880
DOI:10.1109/ICIP.2009.5413391