By Topic

An optimal wavelet for raw SAR data compression

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
$33 $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

3 Author(s)
A. E. Boustani ; Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada ; K. Brunham ; W. Kinsner

Synthetic aperture radar (SAR) is a sophisticated remote sensing tool that is capable of providing high resolution images from a moving platform. Due to the very poor correlation and high entropy of SAR raw data, redundancy reduction techniques have not proven successful and a lossy compression is necessary. In a previous work, we have presented a compression of the raw SAR signal using five kinds of wavelets. The quality reconstruction was very good, however, due to noise like characteristics of the raw SAR signal, none of the standard wavelets was very efficient in compacting energy in the transform domain. In this paper, we propose to determine an optimal 2-D wavelet which is learned directly from the raw SAR data. The optimality criterion in the learning processes is redundancy minimization in the transform domain. Experiments show that this optimal wavelet performs better than the standard wavelets.

Published in:

Electrical and Computer Engineering, 2003. IEEE CCECE 2003. Canadian Conference on  (Volume:3 )

Date of Conference:

4-7 May 2003