# Transformer Self-Attention Change Detection Network with Frozen Parameters
## 1 Introduction  
Change detection identifies differences between two images captured at different times at the same location; it labels the pixels in the changed areas as 0 and the pixels in the unchanged areas as 1. With the ongoing advancement of remote sensing technology, change detection in remote sensing images has become a key focus in the field [1]. In addition, change detection in remote sensing images plays a significant role in various fields such as urban development planning [2], agricultural surveys [3, 4], land management [5], and more. [PERSON] [6] introduced the difference method in 1977 to distinguish differences in remote sensing images, marking the beginning of traditional change detection methods. Since then, numerous scientific advancements have been made in this area, including methods such as change vector analysis [7], post-classification refinement [8], and principal component analysis [9]. These methods are capable of effectively distinguishing differences in remote sensing images within a short period of time, significantly improving theefficiency. However, as sensors continue to advance, remote sensing images are achieving higher resolutions, resulting in greater diversity in the appearance, color, and brightness of different land features. Therefore, the detection accuracy of traditional change detection methods is often unsatisfactory. In this context, it is highly meaningful to develop a change detection method that can quickly and accurately adapt to high-resolution remote sensing images. AlexNet, proposed by [PERSON] et al. [10], achieved the championship in the ImageNet image classification competition, demonstrating the powerful feature extraction capability of deep learning-based image processing methods at that time. Moreover, with the continuous advancement of hardware technology, deep learning-based image processing methods quickly realized end-to-end training, showing significant advantages in terms of efficiency and speed. Subsequently, deep learning-based image processing methods experienced rapid development and were also applied in the field of change detection in remote sensing images. [PERSON] et al. [11] proposed a novel deep learning-based method for image change detection; it demonstrated the effectiveness of convolutional neural networks (CNNs) in change detection tasks. In 2018, [PERSON] et al. [12] introduced three convolutional neural network (CNN) architectures specifically designed for change detection tasks. Additionally, they proposed a novel Siamese network structure with shared weights for processing two remote sensing images, achieving the best scores on two datasets. The Siamese network structure subsequently became one of the mainstream methods in the field of change detection. These convolution-based methods exhibit good adaptability in handling various sizes of images and achieve good accuracy. However, the limited receptive field of convolutional networks restricts their ability to capture broader features, which is a key drawback that convolutional neural networks tend to overlook. Due to the excellent capability of Transformers [13; 14] in extracting global semantic features in the field of natural language processing, [PERSON] et al. [15] applied Transformers to the domain of image processing in 2020, providing new insights for image processing. Subsequently, many networks utilizing Transformers emerged in the field of image processing. However, while Transformers provide the ability to extract global semantic features, they also increase the number parameters. [PERSON] et al. [16] incorporated a sliding window mechanism into Transformer-based networks, which retained the advantages of Transformers while significantly reducing the number of parameters.