By Topic

Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient

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

4 Author(s)
A. A. Cole-Rhodes ; Electr. & Comput. Eng. Dept., Morgan State Univ., Baltimore, MD, USA ; K. L. Johnson ; J. LeMoigne ; I. Zavorin

Image registration is the process by which we determine a transformation that provides the most accurate match between two images. The search for the matching transformation can be automated with the use of a suitable metric, but it can be very time-consuming and tedious. We introduce a registration algorithm that combines a simple yet powerful search strategy based on a stochastic gradient with two similarity measures, correlation and mutual information, together with a wavelet-based multiresolution pyramid. We limit our study to pairs of images, which are misaligned by rotation and/or translation, and present two main results. First, we demonstrate that, in our application, mutual information may be better suited for sub-pixel registration as it produces consistently sharper optimum peaks than correlation. Then, we show that the stochastic gradient search combined with either measure produces accurate results when applied to synthetic data, as well as to multitemporal or multisensor collections of satellite data. Mutual information is generally found to optimize with one-third the number of iterations required by correlation. Results also show that a multiresolution implementation of the algorithm yields significant improvements in terms of both speed and robustness over a single-resolution implementation.

Published in:

IEEE Transactions on Image Processing  (Volume:12 ,  Issue: 12 )