Survey of image registration methods

Abstract

Introduction Automatic image registration, bringing two images into alignment by computing a moderately small set of transformation parameters, might seem a well-defined, limited problem that should have a clear, universal solution. Unfortunately, this is far from the state of the art. With a wide spectrum of applications to diverse categories of data, image registration has evolved into a complex and challenging problem that admits many solution strategies. The growing availability of digital imagery in remote sensing, medicine, and numerous other areas has driven a substantial increase in research in image registration over the past 20 years. This growth in research stems from both this increasing diversity in image sources, as image registration is applied to new instruments like hyperspectral sensors in remote sensing and medical imaging scanners in medicine, and new algorithmic principles, as researchers have applied techniques such as wavelet-based features, information theoretic metrics and stochastic numeric optimization. This chapter surveys the diversity of image registration strategies applied to remote sensing. The objectives of the survey are to explain basic concepts used in the literature, review selected algorithms, give an overall framework to categorize and compare algorithms, and point the reader to the literature for more detailed explanations. While manual and semi-manual approaches are still important in remote sensing, our primary intent is to review research approaches for building fully automatic and operational registration systems. Following the survey article by Brown (1992), we review an algorithm by considering the basic principles from which it is constructed.