Accurately extracting building change regions is of great significance to urban and rural planning, geographic national conditions monitoring, and urban expansion analysis. Traditional remote sensing change detection methods are difficult to adapt to the change detection tasks in complex scenes of remote sensing images. In recent years, deep learning change detection algorithms, which have been widely used in the field of computer vision, have significantly improved efficiency and accuracy compared to traditional methods. However, the features of buildings on remote sensing images are rich and varied, and it is difficult to obtain samples of building changes, which leads to the limited accuracy of existing deep learning models in building change detection tasks. This paper proposes a change attention residual siamese network (CAR-siamese net), which enhances the interaction of image information at different scales, and fully learns the change features of buildings. In addition, a pre-training strategy is proposed in this paper to effectively use building segmentation samples, and the ability of the change detection network to interpret building changes is improved. In this paper, a building change detection data set is made based on images of Changping District, Beijing. Experimental results on this data set and Levir-CD public data set show that the method in this paper can effectively improve the accuracy of building change detection.
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