This talk wil deal with two- and multi-set canonical correlations analysis and their application to data fusion especially with a view to change detection. The talk describes the multivariate alteration detection (MAD) transformation which is based on the established canonical correlation analysis. It also proposes post-processing of the change detected by the MAD variates by means of maximum autocorrelation factor (MAF) analysis. As opposed to most other multivariate change detection schemes the MAD and the combined MAF/MAD transformations are invariant to affine transformations of the originally measured variables. Therefore, they are insensitive to, for example, differences in gain and off-set settings in a measuring device, and to the application of radiometric and atmospheric correction schemes that are linear or affine in the gray numbers of each image band. Other multivariate change detection schemes described are principal component type analysis of simple difference images.
A case study with Landsat TM data using simple linear stretching and masking of the change images shows the usefulness of the new MAD and MAF/MAD change detection schemes. A simple simulation of a no-change situation shows the power of the MAD and MAF/MAD transformations.