Recently, transformer-based change detection methods have achieved remarkable performance by sophisticated architectures for extracting powerful feature representations. However, due to the existence of various noises in bitemporal images, there are problems such as loss of semantic objects and incompleteness that will occur in change detection. The existing transformer-based approaches do not fully address this issue. In this paper, we propose a transformer-based multiscale difference-enhancement U-shaped network and call it TUNetCD, for change detection in remote sensing. The encoder, which is composed of a multilayer Swin-Transformer block structure, can extract multilevel feature maps, further enhance these multilevel feature maps using a Swin-Transformer feature difference map processing module, and finally obtain the final change map using a lightweight decoder. We conducted comprehensive experiments on two publicly available benchmark datasets, LEVIR-CD and DSIFN-CD, to verify the effectiveness of the method, and our method outperformed other advanced transformer-based methods. ? 2022 Gulinazi Ailimujiang et al.
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机译：最近，基于 Transformer 的变化检测方法通过复杂的架构提取强大的特征表示，取得了显著的性能。然而，由于双时相图像中存在各种噪声，因此在变化检测中会出现语义对象丢失和不完备等问题。现有的基于变压器的方法并不能完全解决这个问题。本文提出了一种基于变压器的多尺度差分增强U型网络，并将其命名为TUNetCD，用于遥感中的变化检测。编码器由多层Swin-Transformer块结构组成，可以提取多级特征图，使用Swin-Transformer特征差分图处理模块进一步增强这些多级特征图，最后使用轻量级解码器获得最终的变化图。我们在两个公开可用的基准数据集LEVIR-CD和DSIFN-CD上进行了全面的实验，以验证该方法的有效性，我们的方法优于其他先进的基于Transformer的方法。?2022 古林纳齐 爱丽木江 et al.
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