Stochastic Parallel Gradient Descent (SPGD) image matching method is a new image matching method, which simultaneously imposes statistically independent random disturbances on all of the deformation parameters and use correlation coefficient stochastic gradient component to carry out an iterative operation instead of real gradient component, so that it can obtain quick optimum parameter estimation. The selection of the disturbance amplitudes and the gain coefficients is the key issue that needs to be solved in this method. Presently, the question that, if we could select the same default parameters of universal adaptability in different deformation situation, or if we should update parameters to achieve the optimal matching results, has not got a better answer yet. On the one hand, this method’s utmost matching performance is explored in different preset conditions; on the other hand, it elementarily summarizes the laws of the impact on this method’s matching performance by selection of different disturbance amplitudes and gain coefficients. On the basis of the above, the grading parameters updating strategy is studied, so that this issue has been further addressed.