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  • Cited by 1
  • Print publication year: 2011
  • Online publication date: May 2011

6 - Image registration using mutual information

from PART II - Similarity Metrics for Image Registration

Summary

Abstract

This chapter provides an overview of the use of mutual information (MI) as a similarity measure for the registration of multisensor remote sensing images. MI has been known for some time to be effective for the registration of monomodal, as well as multimodal images in medical applications. However, its use in remote sensing applications has only been explored more recently. Like correlation, MI-based registration is an area-based method. It does not require any preprocessing, which allows the registration to be fully automated. The MI approach is based on principles of information theory. Specifically, it provides a measure of the amount of information that one variable contains about the other. In registration, we are concerned with maximizing the dependency of a pair of images. In this context, we discuss the computation of mutual information and various key issues concerning its evaluation and implementation. These issues include the estimation of the probability density function and computation of the joint histogram, normalization of MI, and use of different types of interpolation, search and optimization techniques for finding the parameters of the registration transformation (including multiresolution approaches).

Introduction

In this chapter, we discuss the application of mutual information (MI) as a similarity metric for cross-registering images produced by different imaging sensors. These images may be from different sources taken at different times and may be produced at different spectral frequencies and/or at different spatial resolutions.

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