In order to improve the effectiveness of reference map production, it is necessary to predict matching performance for early remote sensing images. For edge feature, the saliency and stability of edge determines matching performance. An algorithm for remote sensing image matching performance was proposed based on block difference of inverse probabilities and texture cell cooccurrence matrix. Firstly, remote sensing image was divided into potential matching regions and no matching regions based on edge density, and training images were extracted from potential matching regions. Secondly, edge feature vector was computed by block difference of inverse probabilities and texture cell cooccurrence matrix (BDIPTCCM). Thirdly, on basis of the real matching probability computed by simulation experiment, matching probability predicting model was built by support vector regression based on edge feature vector. Lastly, matching probability was predicted for the whole remote sensing image based on the matching probability predicting model. The experimental esult shows that the mean squared error between the predicted matching probability and real matching probability is small and the squared correlation coefficient is high. The model is general for the same satellite images after gray level correction. It can meet the demand of remote sensing image matching performance measure, and provide decision support for selecting matching algorithm.