The most common challenge of automatically unsupervised change detection in multitemporal, remotely sensing image is to find the best global threshold in difference images. It is obvious that different results will be obtained according to different threshold selection algorithms. In this paper, a decision fusion method based on maximum joint probability(MJP) is proposed in order to deal with this problem. By means of analyzing the relationship between the detection probability of each detection algorithm and that of final correct, several different results can be fused efficiently by MJP decision fusion algorithm. By taking multitemporal TM images at Wuhan, China, as experimental data, the different images are obtained by correlation coefficient. An array of change detection results are developed by a serial threshold selection algorithms. Final change detection results are gotten by fused results array. Experimental results show that, compared with each threshold selection algorithm, MJP fusion algorithm is more robust and satisfactory. The result confirms the validity of the proposed approach and the opproach is useful in land cover change detection.