Relative radiometric normalization performance for change detection from multi-date satellite images

Abstract

Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in surface reflectance. Five methods of RRN have been applied to 1973, 1983, and 1988 Landsat MSS images of the Atlanta area for evaluating their performance in relation to change detection. These methods include pseudoinvariant features (PIF), radiometric control set (RCS), image regression (IR), no-change set determined from scattergrams (NC), and histogram matching (HM), all requiring the use of a reference-subject image pair. They were compared in terms of their capability to improve visual image quality and statistical robustness. The way in which different RRN methods affect the results of information extraction in change detection was explored. It was found that RRN methods which employed a large sample size to relate targets of subject images to the reference image exhibited a better overall performance, but tended to reduce the dynamic range and coefficient of variation of the images, thus undermining the accuracy of image classification. It was also found that visually and statistically robust RRN methods tended to substantially reduce the magnitude of spectral differences which can be linked to meaningful changes in landscapes. Finally, factors affecting the performance of relative radiometric normalization were identified, which include land-use/ land-cover distribution, water-land proportion, topographic relief, similarity between the subject and reference images, and sample size.