Image registration using a 2nd order Stochastic optimization of mutual information

被引：0
|
作者：
Cole-Rhodes, A [1 ]
Johnson, K [1 ]
Le Moigne, J [1 ]
机构：
[1] Morgan State Univ, Dept Elect & Comp Engn, Baltimore, MD 21251 USA
来源：
IGARSS 2003: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS I - VII, PROCEEDINGS: LEARNING FROM EARTH'S SHAPES AND SIZES | 2003年
关键词：
image registration; mutual information; remote sensing imagery; stochastic optimization; wavelets;
D O I：
暂无
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
In this paper, we extend the stochastic gradient optimization used in a mutual information-based registration algorithm [2] to include second-order (Hessian) effects with the aim of accelerating its convergence rate. We consider images, which are misaligned by a four parameter rigid transformation, consisting of scale, rotation and/or x- and y-translations, and we present the results of optimization using a second-order stochastic derivative. The algorithm is applied to a pair of multi-temporal satellite images, and is implemented in a multi-resolution manner using wavelets. Results are presented for an implementation, which switches after a fixed number of iterations, from the first-order scheme [1,2] to the second-order one.
引用
收藏
页码：4038 / 4040
页数：3
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