Paper Info
Title
Classification of water regions in SAR images using level sets and non-parametric density estimation
Abstract
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.
Year
DOI
Venue
2009
10.1109/ICIP.2009.5413391
ICIP
Keywords
Field
DocType
geophysical image processing,hydrological techniques,image segmentation,remote sensing by radar,synthetic aperture radar,Parzen windows,SAR image classification,automatic topology adaptation,coastline extraction problem,image segmentation,level set methods,nonparametric density estimation,probability density function,river mapping problem,semisupervised algorithm,synthetic aperture radar images,water region classification,Synthetic aperture radar,image segmentation,level set methods,non-parametric density estimation
Kernel (linear algebra),Density estimation,Computer vision,Pattern recognition,Computer science,Synthetic aperture radar,Level set,Image segmentation,Regularization (mathematics),Pixel,Artificial intelligence,Probability density function
Conference
Citations 
PageRank 
References 
0
0.34
11
Authors
2
Name
Order
Citations
PageRank
Margarida Silveira1788.54
Sandra Heleno252.00