There are a lot of differences in multi-source remote sensing images from various sensors about the same scene. Maximization of mutual information can be used for the multi-source image matching

but the accuracy and efficiency of image matching need to be further improved. Therefore

an algorithm for multi-source remote sensing image matching was proposed in this paper

based on contourlet transform

Tsallis entropy based mutual information and improved particle swarm optimization. Firstly

the target image and reference image were decomposed to the low resolution image using contourlet transform

respec-tively. Then

a new image similarity measure criterion

the Tsallis entropy based mutual information

was used to achieve the global optimization. Meanwhile

a modified extremum disturbed and simple particle swarm optimization algorithm was applied to match the lowest resolution remote sensing images. Based on the preliminary result

the matching between the higher resolu-tion images could be implemented stepwise up to the full resolution images. The experimental results show that

compared with those of other existing remote sensing image matching methods

the proposed algorithm has the high accuracy

strong robustness and requires much fewer operations.