Book contents
- Frontmatter
- Contents
- List of contributors
- Foreword by Jón A. Benediktsson
- Acknowledgements
- PART I The Importance of Image Registration for Remote Sensing
- 1 Introduction
- 2 Influence of image registration on validation efforts
- 3 Survey of image registration methods
- PART II Similarity Metrics for Image Registration
- PART III Feature Matching and Strategies for Image Registration
- PART IV Applications and Operational Systems
- PART V Conclusion
- Index
- Plate section
- Plate section
- References
2 - Influence of image registration on validation efforts
from PART I - The Importance of Image Registration for Remote Sensing
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
- 1 Introduction
- 2 Influence of image registration on validation efforts
- 3 Survey of image registration methods
- PART II Similarity Metrics for Image Registration
- 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
Currently, several high level satellite-based data products relevant to Earth science system and global change research are derived from medium spatial resolution remote sensing observations. Because of their high temporal frequency and important spatial coverage, these products, also called biophysical variables, are very useful to describe the mass and energy fluxes between the Earth's surface and the atmosphere. In order to check the relevance of using such products as inputs into land surface models, it is of importance to assess their accuracy. Validation activity consists in evaluating by independent means the quality of these biophysical variables. Currently, these variables are derived from coarse-resolution remote sensing observations. Validation methods consist in generating a ground truth map of these products at high spatial resolution. It is produced by using ground measurements of the biophysical variable and radiometric data from a high spatial resolution sensor. The relationship between biophysical variable and radiometric data, called the transfer function, allows extending the local ground measurements to the whole high spatial resolution image. The resulting biophysical variable map is aggregated to be compared with the medium spatial resolution satellite biophysical products. Three important geometrical issues influence the validation results: (1) The localization error of the local ground measurements, (2) the localization error between high and medium resolution images, and (3) the point spread function (PSF) associated with the medium spatial resolution remote sensing observations. This work aims at analyzing the influence of these issues within the validation process.
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- Image Registration for Remote Sensing , pp. 24 - 34Publisher: Cambridge University PressPrint publication year: 2011