Cart (Loading....) | Create Account
Close category search window

SAR Image Segmentation Using GHM-Based Dirichlet Process Mixture Models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Li Sun ; Sch. of Comput. Sci., Northwestern Polytech. Univ., Xi''an, China ; Yanning Zhang ; Guangjian Tian ; Miao Ma

This paper proposes a robust SAR image segmentation scheme for SAR images with speckle noise. Our method can simulate the intrinsic property of SAR image by the proposed infinite mixture model-Dirichlet process mixture model and determine the cluster number automatically. The Gaussian-Hermite moment is applied to extract features to improve the robust of segmentation and reduce the influence of speckle noise. The effectiveness of proposed method is demonstrated via experiments with the simulated data and real data.

Published in:

Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on  (Volume:1 )

Date of Conference:

24-26 April 2009

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.