Remote sensing image change detection is one of the hotspots of remote sensing application research. It has been widely used in urban change, environmental monitoring, land use, and basic geographic database update. Change detection is the feature and process of quantitative analysis and determination of surface changes from remote sensing data in different periods. The specific work is to analyze two or more images of different phases in the same region, and to detect the changed parts and unchanged parts. In this study, a change detection method based on stack noise reduction automatic encoder network is proposed. The deep learning algorithm applied to SAR (Synthetic Aperture Radar) satellite image change detection is improved, which is suitable for high-resolution remote sensing satellite image, and then improved on the structure of twin network. A change detection method based on branch convolutional neural network is proposed. Finally, the design algorithm removes the false changes such as shadow interference and noise, and tests it on the actual production data image of the high-resolution satellite 2 (GF-2). It has achieved sound results.