Relative Radiometric Normalization Performance for Change Detection from Multi-Date Satellite Images

Relative radiometric normalization (RRN minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in sudace reflectance. Five methods of RRN have been applied to 1973, 1983, and 1988 Landsat MSS images of the Atlanta area for evaluating their pedormance in relation to change detection. These methods include pseudoinvariant features (PIF), radiometric control set (RCS), image regression ( m l , no-change set determined from scattergrams (NC], and histogram matching (MM), 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 pedormance 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. Introduction Spectral data acquired by satellite sensors are influenced by a number of factors, such as atmospheric absorption and scattering, sensor-target-illumination geometry, sensor calibration, and image data processing procedures, which tend to change through times (Teillet, 1986). Targets in multi-date scenes are extremely variable and have been nearly impossible to compare in an automated mode (Kim and Elman, 1990). In order to detect genuine landscape changes as revealed by changes in surface reflectance from multi-date satellite images, it is necessary to carry out radiometric correction. Two approaches to radiometric correction are possible: absolute and relative (Lo and Yang, 1998). The absolute approach requires the use of ground measurements at the time of data acquisition for atmospheric correction and sensor calibration. This is not only costly but also impractical when archival satellite image data are used for change analysis (Hall et al., 1991). The relative approach to radiometric correction, known as relative radiometric normalization (RRN), is preferred because no in situ Department of Geography, University of Georgia, Athens, GA 30602 (yang&ga.edu). OGRAMMnI; iNGll atmospheric data at the time of satellite overpasses are required. This method involves normalizing or rectifying the intensities or digital numbers (DN) of multi-date images bandby-band to a reference image selected by the analyst. The normalized images would appear as if they were acquired with the same sensor under similar atmospheric and illumination conditions to those of the reference image. In connection with the fund funded project ATLANTA (ATlanta Land-use ANalysis: Temperature and Air-quality) which necessitates accurately mapping changes in the landuselland-cover of the Atlanta Metropolitan Region, Georgia, for the past 25 years using Landsat MSS data, the RRN approach is the reasonable choice. Different RRN methods developed for radiometric correction of multi-date satellite images were evaluated to identify the right one for this project. The only published work of a systematic comparison was carried out by Yuan and Elvidge (1996), who applied seven empirical RRN methods to two Landsat MSS images of the Washington, D.C. area. Their comparisons were made visually, using a measure of agreement based on standard error statistics. Despite the excellent effort of Yuan and Elvidge, the performance of the different RRN methods deserves a more thorough analysis for the following three reasons. First, it is noted that the Landsat MSS images of the Washington, D.C. area used by Yuan and Elvidge contain a high proportion of clear water and urban area, which should favor methods relying on these ground targets in determining the transformation coefficients. In contrast, the Landsat M s s data for the Atlanta region contain a much smaller fraction of water and urban area but a larger proportion of forest and cropland distributed over a foothill and mountainous area. Therefore, these RRN methods may perform differently when applied to images with more diverse geographical features. Understanding the variation of RRN performance with respect to different scene conditions is important for reinforcing the absolute and comparative utility of these methods. Second, the effects of alternative radiometric normalization methods upon the outcomes of change detection are unknown. To bridge this gap, possible impacts of different RRN methods on image classification and spectral change detection will be examined. Finally, it is believed that there are factors that may affect the performance of radiometric normalization other than the RRN methods themselves. By comparing the performance variations of RRN methods as applied to two reference-subject image pairs with different levels of similarity, this Photogrammetric Engineering & Remote Sensing Vol. 66, No. 8, August 2000, pp. 967-980. 0099-1112IOOI6608-967$3.00/0