# Transformer Self-Attention Change Detection Network with Frozen Parameters
## 2 Methodology  
### Overall Framework  
Indeed, CNN and Transformer methods are two prominent methods in the field of image processing [22]. CNNs excel in extracting local features, while Transformers are known for their ability to capture global features. Combining these two approaches can be a promising choice, because it leverages the strengths of both methods. Additionally, we introduce the method of parameter freezing, where we freeze the parameters of certain modules in the pre-trained network and combine them with the untrained network to form a new network. This approach resembles a residual-like structure, where the untrained network essentially learns to fit the difference between the frozen parameters and the optimal solution. Based on the aforementioned points, we propose the frozen-parameter Transformer self-attention change detection network. This network combines the powerful feature extraction capabilities of Transformers and the parameter-freezing technique to improve change detection performance. Figure 1 provides a detailed illustration of the overall framework of the ZAQNet. This paper aims to propose a Transformer self-attention change detection network, ZAQNet, based on freezing parameters, which is used to detect change regions more accurately. In addition, this paper also introduces four innovative modules: the GIAU (Global Information Aggregation Unit), GSAU (Global Spatial Attention Unit), GSCU (Global Channel Attention Unit), and PRU (Pooling Refinement Unit). As shown in Figures 2-5, these modules optimize the performance of the model from the aspects of feature fusion, spatial dimension attention, channel dimension attention, and spatial position refinement. The GIAU accurately locates the change area by fusing the features of dual-temporal remote sensing images. The GSAU implements a self-attention mechanism in the spatial dimension to enhance the model's ability to capture global change information; the GSCU implements self-attention operation in the channel dimension to strengthen the model's attention to the change feature map. The PRU further improves the recovery effect of the feature map by extracting and refining the spatial location information of the underlying feature map. In the downsampling stage, we employ two pairs of ResNets with shared weights to extract features from the two remote sensing images. Notably, one of the ResNet pairs undergoes pre-training, and its parameters are subsequently frozen. Afterwards, the feature maps obtained from the two pairs of ResNets are summed together and fed into the Feature Fusion Unit (FFU) for further processing. The specific operations are illustrated in the pink region of Figure 1. The features extracted by the ResNet pairs with frozen parameters are fed into the FFU with frozen parameters for processing, while the features extracted by the ResNet pairs involved in training are passed through the FFU involved in training. Finally, the processed feature maps are summed and fed into the upsampling stage to assist the model in generating the mask image. The specific operations are depicted in the blue region of Figure 1. Additionally, within the FFU, we introduce three auxiliary units--the GIAU, GSAU, and GCAU--to process the extracted features at different levels. The internal structure of the FFU is depicted in the flaxen region of Figure 1. Furthermore, in Table 1, the specific details of the parameters in the model are shown.