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

页码：4038 / 4040

页数：3

共 5 条

[1] Multiresolution registration of remote-sensing images using stochastic gradient [J].