Relative Radiometric Normalisation - performance testing of selected techniques and impact analysis on vegetation and water bodies

Abstract

In this paper, six image-based Relative Radiometric Normalization (RRN) techniques were applied to normalize the bi-temporal Landsat 5 TM data-set. RRN techniques do not require any atmospheric and ground information at the time of image acquisition. The target image for the year 2009 was normalized in such a way that it resembled the atmospheric and sensor conditions similar to those under which the reference image of the same season for the year 1990 was acquired. Among the selected methods applied, it was found that the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD) method performed better, based on the error statistic. The IR-MAD technique was found to be advantageous as it identified a large set of true time-invariant pixels automatically from the change background using iterative canonical component analysis. The technique also stretches the values of Normalized Difference Vegetation Index and Normalized Difference Water Index and may help to distinguish different vegetation and water bodies better.

Keywords:

• Radiometric normalization
• MODTRAN
• IR-MAD
• pseudo invariant features

Acknowledgements

The authors are indebted to the University of Southampton, UK for providing excellent infrastructure to conduct the research work.