Total least squares for anomalous change detection

James Theiler; Anna M. Matsekh

doi:10.1117/12.851935

12 May 2010 Total least squares for anomalous change detection

James Theiler, Anna M. Matsekh

Proceedings Volume 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI; 76951H (2010) https://doi.org/10.1117/12.851935
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

James Theiler and Anna M. Matsekh "Total least squares for anomalous change detection", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76951H (12 May 2010); https://doi.org/10.1117/12.851935

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