Book contents
- Frontmatter
- Contents
- List of contributors
- Foreword by Jón A. Benediktsson
- Acknowledgements
- PART I The Importance of Image Registration for Remote Sensing
- PART II Similarity Metrics for Image Registration
- PART III Feature Matching and Strategies for Image Registration
- 7 Registration of multiview images
- 8 New approaches to robust, point-based image registration
- 9 Condition theory for image registration and post-registration error estimation
- 10 Feature-based image to image registration
- 11 On the use of wavelets for image registration
- 12 Gradient descent approaches to image registration
- 13 Bounding the performance of image registration
- PART IV Applications and Operational Systems
- PART V Conclusion
- Index
- Plate section
- Plate section
- References
11 - On the use of wavelets for image registration
from PART III - Feature Matching and Strategies for Image Registration
Published online by Cambridge University Press: 03 May 2011
Book contents
- Frontmatter
- Contents
- List of contributors
- Foreword by Jón A. Benediktsson
- Acknowledgements
- PART I The Importance of Image Registration for Remote Sensing
- PART II Similarity Metrics for Image Registration
- PART III Feature Matching and Strategies for Image Registration
- 7 Registration of multiview images
- 8 New approaches to robust, point-based image registration
- 9 Condition theory for image registration and post-registration error estimation
- 10 Feature-based image to image registration
- 11 On the use of wavelets for image registration
- 12 Gradient descent approaches to image registration
- 13 Bounding the performance of image registration
- PART IV Applications and Operational Systems
- PART V Conclusion
- Index
- Plate section
- Plate section
- References
Summary
Abstract
Wavelets provide a multiresolution description of images according to a well-chosen division of the space-frequency plane. This description provides information about various features present in the images that can be utilized to perform registration of remotely sensed images. In the last few years, many wavelet filters have been proposed for applications such as compression; in this chapter, we review the general principle of wavelet decomposition and the many filters that have been proposed for wavelet transforms, as they apply to image registration. In particular, we consider orthogonal wavelets, spline wavelets, and two pyramids obtained from a steerable decomposition. These different filters are studied and compared using synthetic datasets generated from a Landsat-Thematic Mapper (TM) scene.
Introduction
The main thrust of this chapter is to describe image registration methods that focus on computational speed and on the ability of handling multisensor data. As was described in Chapter 1 and in Brown (1992), any image registration method can be described by a feature space, a search space, a search strategy, and a similarity metric. Utilizing wavelets for image registration not only defines the type of features that will be matched, but it also enables the matching process to follow a multiresolution search strategy. Such an iterative matching at multiple scales represents one of the main factors that will define the accuracy of such methods.
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- Image Registration for Remote Sensing , pp. 240 - 264Publisher: Cambridge University PressPrint publication year: 2011
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