Paper Info
Title
A New Region-Based Active Contour Model With Skewness Wavelet Energy For Segmentation Of Sar Images
Abstract
A new method of segmentation for Synthetic Aperture Radar (SAR) images using the skewness wavelet energy has been presented. The skewness is the third order cumulant which measures the local texture along the region-based active contour. Non linearity in intensity inhomogeneities often occur in SAR images due to the speckle noise. In this paper we propose a region-based active contour model that is able to use the intensity information in local regions and to cope with the speckle noise and nonlinear intensity inhomogeneity of SAR images. We use a wavelet coefficients energy distribution to analyze the SAR image texture in each sub-band. A fitting energy called skewness wavelet energy is defined in terms of a contour and a functional so that, the regions and their interfaces will be modeled by level set functions. A functional relationship has been calculated on these level sets in terms of the third order cumulant, from which an energy minimization is derived. Minimizing the calculated functions derives the optimal segmentation based on the texture definitions. The results of the implemented algorithm on the test images from the Radarsat SAR images of agricultural and urban regions show a desirable performance of the proposed method.
Year
DOI
Venue
2010
10.1587/transinf.E93.D.1690
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
segmentation, synthetic aperture radar, active contours, level set method, third order cumulant
Active contour model,Computer vision,Skewness,Pattern recognition,Level set method,Synthetic aperture radar,Image texture,Computer science,Level set,Artificial intelligence,Speckle noise,Wavelet
Journal
Volume
Issue
ISSN
E93D
7
1745-1361
Citations 
PageRank 
References 
1
0.38
19
Authors
3
Name
Order
Citations
PageRank
Gholamreza Akbarizadeh1516.19
Gholam Ali Rezairad210.72
Shahriar Baradaran Shokouhi392.00