## 1 Introduction  
Remote image change detection (CD) is the process of obtaining semantic change information such as vegetation and buildings from analyzing multitemporal remote images taken in the same location at different times. Lately, due to the advancement of high-resolution remote images, CD has been broadly employed for disaster monitoring [1, 2], in which CD can discover the scope of the damage, so that the rescue and relief personnel can be reasonably arranged and dispatched; urban expansion [3], in which CD can identify alterations in and demolitions of urban buildings and detect the presence of unauthorized buildings; forest and vegetation changes [4], in which CD can effectively identify the growth change areas of forest and vegetation; and many other aspects, which have also attracted more and more scholars to be more interested in this task and to produce a lot of work. The process of CD is shown in Figure 1.  
Deep learning also has a very promising future in the domain of CD. Owing to the excellent characteristic extraction ability of convolutional neural networks (CNNs), some early CD algorithms have used CNNs to extract bitemporal features [5; 6; 7; 8; 9; 10; 11; 12] to complete CD. [PERSON] et al. [6] established a dual-attention convolutional siamese network, which processed two input images with shared weights and firstly introduced the siamese construction composed of two identical structures in the CD task. However, its loss function only improved the data imbalance and did not effectively solve the problems of pseudo-change and the difficult detection of boundary regions. [PERSON] et al. [7] first proposed codec-based fully convolutional neural networks (FCNNs), which replaced the fully connected layer with a convolutional layer and could receive inputs of arbitrary size. As none of these methods could obtain the global information of the images due to the local limitations of traditional convolutional feature extraction, some subsequent works made some improvements in this regard as well. [PERSON] et al. [8] put forward the UNet++ Multiple Side-Outputs Fusion network based on UNet++ [13], which fused deep supervision and dense connection mechanisms to optimize the edge details of change regions. In addition, UNet++ consists of different depths of UNet, providing improved segmentation performance for objects of different sizes. Yet, this method ignored the effect of season, light, etc., on change detection. [PERSON] and [PERSON] [10] proposed the pyramid spatial-temporal attention module that modeled the spatial-temporal relationships during the feature extraction phase and considered capturing multiscale spatial-temporal relationships to extract more discriminative features. In [11], a deep-supervised image fusion algorithm was presented to optimize the boundary integrity and compactness inside the target by means of merging multilayer depth features and differential image features. In [12], channel and spatial attention were used when processing images at each moment to obtain more discriminative features. [PERSON] et al. [9] utilized dilated convolution to enlarge the receptive field, where the dilated convolution was conducted by setting the dilated rate to fill the conventional convolution kernel with 0. [PERSON] et al. [14] adopted UNet++, where features of different levels were closely interconnected in a bottom-up manner to yield fine-grained change maps. These methods somewhat improved the disadvantages of traditional convolution, but they still could not fully extract global information, neither could they accurately identify large-scale objects nor perform well enough to acquire the correlations between the surface objects and the rest of the objects on the entire image. Figure 1: The process of CD. The Transformer [15] has been gradually applied in the domain of computer vision [16; 17; 18; 19] due to its superior ability to capture long-term dependencies.