Performance of correlation approaches for the evaluation of spatial distortion reductions

Abstract

The analysis of optical remote sensing images often requires a perfect pixel alignment between single bands. Even smallest deviations may degrade the accuracy of subsequent parameter retrieval or lead to the detection of non-existing structures caused by artificial gradients. Hence, a careful pre-processing is essential for minimizing spatial non-uniformities such as erroneous co-registration. The results need to be validated and assigned with a quality flag that is unfortunately still not a common practice. In this letter, four broadly used global correlation approaches, a two-dimensional Gaussian peak fit, a poly phase technique, an iterative phase approach and its proposed enhancement, were tested for their capacity to serve either as an evaluation tool for preceding spatial distortion reductions, e.g. by co-registration, or as a global minimizer for generic reduction approaches. For this eight broadly used test images, three Landsat 7 and three Landsat 8 samples were sub pixel shifted artificially and degraded by different noise levels resulting in more than 200,000 noise and shift scenarios. Additionally, one state-of-the-art approach was enhanced by 50% on average for all scenarios and by 280% on average for all non-degraded images. This study indicates that three out of four approaches can serve as evaluation tools for spatial distortion reductions or as a global minimizer even for highly degraded images, whereas proposed enhancement offers highest accuracy and the poly phase approach offers best overall performance with regard to considered criteria.

Acknowledgements

We thank both US institutions, the UC SIPI and NASA’s Land Processes Distributed Active Archive Center (LP DAAC), for providing image samples and Landsat satellite images. We are grateful to anonymous reviewers and the Editor for their constructive and insightful comments that helped to improve the quality of the manuscript.

Funding

This work was funded by the German Federal Ministry of Economics and Technology [BMWI 50EE1012/EnMAP] within the framework of EnMAP (Environmental Mapping and Analysis Program).