Total Least Squares for Anomalous Change Detection

摘要

A family of subtraction-based anomalous change detection algorithms is derived from a total least squares(TLSQ) framework. This provides an alternative to the well-known chronochrome algorithm, which is derivedfrom ordinary least squares. In both cases, the most anomalous changes are identified with the pixels that exhibitthe largest residuals with respect to the regression of the two images against each other. The family of TLSQ-based anomalous change detectors is shown to be equivalent to the subspace RX formulation for straight anomalydetection, but applied to the stacked space. However, this family is not invariant to linear coordinate transforms.On the other hand, whitened TLSQ is coordinate invariant, and special cases of it are equivalent to canonicalcorrelation analysis and optimized covariance equalization. What whitened TLSQ offers is a generalization ofthese algorithms with the potential for better performance.