Total least squares for anomalous change detection
2010
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 derived
from ordinary least squares. In both cases, the most anomalous changes are identified with the pixels that exhibit
the largest residuals with respect to the regression of the two images against each other. The family of TLSQbased
anomalous change detectors is shown to be equivalent to the subspace RX formulation for straight anomaly
detection, 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 canonical
correlation analysis and optimized covariance equalization. What whitened TLSQ offers is a generalization of
these algorithms with the potential for better performance.
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