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
- PART IV Applications and Operational Systems
- 14 Multitemporal and multisensor image registration
- 15 Georegistration of meteorological images
- 16 Challenges, solutions, and applications of accurate multiangle image registration: Lessons learned from MISR
- 17 Automated AVHRR image navigation
- 18 Landsat image geocorrection and registration
- 19 Automatic and precise orthorectification of SPOT images
- 20 Geometry of the VEGETATION sensor
- 21 Accurate MODIS global geolocation through automated ground control image matching
- 22 SeaWiFS operational geolocation assessment system
- PART V Conclusion
- Index
- Plate section
- Plate section
- References
17 - Automated AVHRR image navigation
from PART IV - Applications and Operational Systems
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
- PART III Feature Matching and Strategies for Image Registration
- PART IV Applications and Operational Systems
- 14 Multitemporal and multisensor image registration
- 15 Georegistration of meteorological images
- 16 Challenges, solutions, and applications of accurate multiangle image registration: Lessons learned from MISR
- 17 Automated AVHRR image navigation
- 18 Landsat image geocorrection and registration
- 19 Automatic and precise orthorectification of SPOT images
- 20 Geometry of the VEGETATION sensor
- 21 Accurate MODIS global geolocation through automated ground control image matching
- 22 SeaWiFS operational geolocation assessment system
- PART V Conclusion
- Index
- Plate section
- Plate section
- References
Summary
Abstract
To enable automated (without human intervention) AVHRR (Advanced Very High Resolution Radiometer) image navigation, a base image is defined and the maximum cross-correlation (MCC) method is used to automatically compute the satellite attitude parameters required to geometrically correct images to this base image. The auto attitude corrections are shown to be more accurate than the traditional linear translation methods and provide a significant improvement in geolocation accuracy over two other AVHRR image navigation methods. Geolocation accuracies are given for near-real-time use of this method for operational applications using daily imagery off the U.S. East and West Coasts. A further application of the attitude corrections is demonstrated whereby attitude corrections computed over land can be carried forward in the satellite's orbit to accurately navigate imagery over the open ocean where map reference points are not available.
Introduction
The accurate georegistration of satellite imagery typically requires the application of an orbital model to predict the location of the spacecraft, as well as an instrument pointing model to determine the geolocation of the sensor field of view (FOV) (Rosborough et al., 1994). The implementation of these two models is straightforward and easily automated. However, the obtained registration accuracy is dependent on the accuracies of the timing of the data and the spacecraft attitude (roll, pitch, and yaw) (Rosborough et al., 1994; Baldwin and Emery, 1994).
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- Image Registration for Remote Sensing , pp. 383 - 399Publisher: Cambridge University PressPrint publication year: 2011