Concepts, measures, and models of image quality are shown to be quite important in pattern recognition applications. Pattern recognition of imagery subjected to geometrical differences (such as scale and rotational changes) and intensity differences (such as arise in multispectral imagery) are considered. After modeling these image differences as a stochastic process, the optimal filter is derived. This filter is shown to be the principal component of the data. This pattern recognition algorithm is verified using multi-sensor imagery, and the results are found to compare favorably to those obtained using other candidate techniques.
B. V. K. Vijaya Kumar,
"Principal-Component Imagery For Statistical Pattern Recognition Correlators," Optical Engineering 21(1), 210143 (1 February 1982). https://doi.org/10.1117/12.7972856