With the development of deep learning technology, high-resolution remote sensing image change detection based on deep learning has become a hot topic in the field of remote sensing. However, the existing change detection methods based on deep learning only detect the change area of a specific object, and there is no public multi-objects change detection dataset. Focus on these problems, this paper proposed an end-to-end method to obtain the change detection results with change types for high resolution remote sensing images, including sample generation and a deep-learning network, called Res-Unet. Firstly, we obtain the label data by manual annotating. Then, co-registered image pairs are concatenated as an input for the network, and the multi-objects change detection results are directly generated by the network. The experimental results show that the method is effective and Res-Unet has a higher FWloU scores than U-Net.