  In remote sensing image-blurring is induced by many sources such as
atmospheric scatter, optical aberration, spatial and temporal sensor
integration. The natural blurring can be exploited to speed up target search by
fast template matching. In this paper, we synthetically induce additional
non-uniform blurring to further increase the speed of the matching process. To
avoid loss of accuracy, the amount of synthetic blurring is varied spatially
over the image according to the underlying content. We extend transitive
algorithm for fast template matching by incorporating controlled image blur. To
this end we propose an Efficient Group Size (EGS) algorithm which minimizes the
number of similarity computations for a particular search image. A larger
efficient group size guarantees less computations and more speedup. EGS
algorithm is used as a component in our proposed Optimizing auto-correlation
(OptA) algorithm. In OptA a search image is iteratively non-uniformly blurred
while ensuring no accuracy degradation at any image location. In each iteration
efficient group size and overall computations are estimated by using the
proposed EGS algorithm. The OptA algorithm stops when the number of
computations cannot be further decreased without accuracy degradation. The
proposed algorithm is compared with six existing state of the art exhaustive
accuracy techniques using correlation coefficient as the similarity measure.
Experiments on satellite and aerial image datasets demonstrate the
effectiveness of the proposed algorithm.
