An object-oriented change detection algorithm was proposed by combining JS(Jensen-Shannon)divergence feature and cross-correlation feature for aerial remote sensing imagery. First,multiscale segmentation was employed to get image objects. Second,JS divergence,which reflects the overall statistical features of gray distribution in each object,and cross-correlation feature,which describes the internal structure changes of each object,was extracted. Third,the decision-level fusion algorithm was applied to fuse the two complementary features to detect the changed area. Finally,the accuracy of the results was compared with that of the fixed-weight fusion algorithm. Experimental results indicate that the average accuracy of the proposed method reaches 93.07 %,the average false detection rate and omission rate are 7.13 % and 4.37 % and that the average accuracy of the proposed method is 8.98 %,4.71 % and 4.20 % higher than those of the algorithms based on JS divergence feature,cross-correlation feature and fixed-weight fusion,respectively. Hence,the proposed method can not only detect the change area effectively,but also improve the accuracy of change detection,which shows the potential and effectiveness of the proposed method in change detection of aerial remote sensing images.