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Remote sensing change detection is an important research direction in the field of remote sensing. It is mainly used to focus on the changing information on the ground over a period of time, and to identify the interested change targets from it. The rapid changes in ground information due to social development undoubtedly increase the importance of change detection. Currently, change detection methods still have some shortcomings in dealing with complex targets, environmental noise, and other aspects. Therefore, we propose a differential feature extraction network based on adaptive frequency transformer for remote sensing change detection (DAFT). Adaptive frequency transformer (AFFormer) is capable of separating change targets and environments from a frequency perspective and capturing long-range dependencies between feature information through self-attention. Therefore, in DAFT, we use AFFormer as the backbone network to extract feature information from bitemporal images, enhancing our focus on change targets while obtaining richer and more detailed information. To our knowledge, this is the first time that AFFormer has been applied in the field of CD. To address the issues of missing location information of change targets and insufficient local feature correlation, DAFT proposes a differential features enhancement module in the feature reconstruction stage of change targets. In addition, DAFT uses DO-Conv to enhance pixel correlation calculation in convolutional operations, allowing the network to focus on richer information. By outputting results at different scales during the feature reconstruction stage, DAFT computes multiple losses that are summed up to guide the training process for better performance. The experimental results prove that DAFT achieves high versus mainstream networks. On LEVIR-CD the F1 is 91.814 and the IoU is 84.866; on WHU-CD the F1 is 92.085 and the IoU is 85.330; on GZ-CD the F1 is 86.065 and the IoU is 74.512.

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