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CHANGE DETECTION IN MULTI-SPECTRAL , BI-TEMPORAL SPATIAL DATA USING ORTHOGONAL TRANSFORMATIONS

This paper introduces the multivariate alteration detection (MAD) transformation which is based on the established canonical correlation analysis. The MAD transformation is invariant to linear scaling. It is therefore insensitive to for example differences in gain settings in a measuring device. Other multivariate change detection schemes described are principal component type analysis of simple difference images. A case study with SPOT HRV data using simple linear stretching and masking of the change images shows the usefulness of the new MAD change detection scheme.