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
- 4 Fast correlation and phase correlation
- 5 Matched filtering techniques
- 6 Image registration using mutual information
- PART III Feature Matching and Strategies for Image Registration
- PART IV Applications and Operational Systems
- PART V Conclusion
- Index
- Plate section
- Plate section
- References
5 - Matched filtering techniques
from PART II - Similarity Metrics for Image Registration
Published online by Cambridge University Press: 03 May 2011
- 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
- 4 Fast correlation and phase correlation
- 5 Matched filtering techniques
- 6 Image registration using mutual information
- PART III Feature Matching and Strategies for Image Registration
- PART IV Applications and Operational Systems
- PART V Conclusion
- Index
- Plate section
- Plate section
- References
Summary
Abstract
A matched filter is a linear filtering device developed for signal detection in a noisy environment. The matched filtering technique can be directly employed in image registration for the estimation of image translation. The transfer function of a classic matched filter is the Fourier conjugate of the reference image, divided by the power spectrum of the system noise. If only the phase term of the reference image is used in the construction of the transfer function, the so-called phase-only matched filter generates a sharper peak at the maximum output than that obtained by a classic matched filter. Thus, the image translation can be detected more reliably. If rotation and scale change are also involved, the images to be registered can first be transformed into the Fourier-Mellin domain. The Fourier-Mellin transform of an image is translation invariant and represents rotation and scale changes as translations in the angular and radial coordinates. Employing matched filtering or phase-only matched filtering on the Fourier-Mellin transforms of the images yields the estimation of the image rotation and scaling. After correcting for rotation and scaling, the image translation can be determined by using the matched filtering or phase-only matched filtering method.
Introduction
Matched filtering is a classic signal processing technique widely used in signal detection (Turin, 1960; Vanderluht, 1969; Whalen, 1971; Kumar and Pochapsky, 1986). A matched filter is a linear filter with a transfer function that maximizes the output signal-to-noise ratio (SNR) for an input signal with known properties.
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- Image Registration for Remote Sensing , pp. 112 - 130Publisher: Cambridge University PressPrint publication year: 2011
References
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