Based on the stationary random properties of remote sensing images, a correlation model is proposed to resolve the effects of the image rotation and translation on the correlation value in scene matching. The rotation invariance is achieved by measuring the image rotation with the model and compensating the rotation before the 2D translation scene matching. The input image is rotated from -5° to 5° at an interval of 1° and 11 new images are generated. The 11 new images correlate with all the template images and eleven correlation matrices are obtained. The maximum values of each correlation matrix are picked up and they could follow a fixed curve predicted by the model. Fitting the curve, the rotation corresponding to the estimated peak of the curve is considered to be the rotation of the input image. The rotation measurement of the input image can be as accurate as 0.05°. With an extra 36 rotations of the input image, the measuring range of rotation can be enlarged into ±180°. This method could be very fast and accurate for scene matching in the parallel multichannel holographic optical correlator.