A least-squares image matching algorithm was extended to adjust the different possible variances, auto-and cross-covariances found in a gray-level covariance matrix. Scaling variance and covariance components were estimated iteratively, based on some positive-semidefinite accompanying matrices. The theory of a best linear, unbiased estimator for the scale factors has been presented in detail. Conjugate-point accuracy results were obtained from 114 pairs of Radarsat-1 crossroads and pond-corner image windows. A 14% improvement in sub-pixel matching accuracy was achieved by adhering to the proposed BLU-Estimating algorithm, in particular for a category of the 11 x 11 image-window size.