By Topic

A Novel Sparse Method for Despeckling SAR Images

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

2 Author(s)
Amirmazlaghani, M. ; Dept. of Comput. Eng. & Inf. Technol., Amirkabir Univ. of Technol., Tehran, Iran ; Amindavar, H.

This paper presents an algorithm for speckle reduction of synthetic aperture radar (SAR) images within a framework of multiscale curvelet analysis. First, we introduce a novel method to investigate the presence of 2-D heteroscedasticity based on Lagrange multiplier procedure. Employing this test confirms the heteroscedasticity of SAR image curvelet coefficients. Therefore, we employ a generalization of 2-D generalized autoregressive conditional heteroscedastic (2-D GARCH) model, called 2-D GARCH generalized Gaussian (2-D GARCH-GG), to these coefficients. This model preserves the appropriate properties of 2-D GARCH for modeling the curvelet coefficients while extending the dynamic formulation of 2-D GARCH model. Then, we design a novel Bayesian processor based on employing 2-D GARCH-GG model to estimate the noise-free curvelet coefficients. Experiments carried out on synthetic SAR images, as well as on true SAR images, verify the performance improvement in utilizing the new strategy compared with other established despeckle algorithms.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:50 ,  Issue: 12 )