[Objective] The U-Net based land use/cover change detection method with high resolution image was introduced to provide theoretical support for the application of the model in remote sensing image change detection. [Methods] The U-type neural network was used to detect the change spots in Gaofen-1 image of Yuzhou City, He’nan Province and WHU building data, and compared with FCN and SegNet. [Results] The experimental results showed that the F₁ score of U-type neural network model were 0.699,0.66 and 0.673 respectively, which were better than the other two methods, and the missing rate was lower, which was closer to the change reference diagram. [Conclusion] It is feasible to use U-type neural network for change detection in high-resolution remote sensing images, and it could have high detection accuracy.