Synthetic aperture radar (SAR) image change detection is a critical task and has received increasing attentions in the remote sensing community. However, existing SAR change detection methods are mainly based on convolutional neural networks (CNNs), with limited consideration of global attention mechanism. In this letter, we explore Transformer-like architecture for SAR change detection to incorporate global attention. To this end, we propose a convolution and attention mixer (CAMixer). First, to compensate the inductive bias for Transformer, we combine self-attention with shift convolution in a parallel way. The parallel design effectively captures the global semantic information via the self-attention and performs local feature extraction through shift convolution simultaneously. Second, we adopt a gating mechanism in the feed-forward network to enhance the non-linear feature transformation. The gating mechanism is formulated as the element-wise multiplication of two parallel linear layers. Important features can be highlighted, leading to high-quality representations against speckle noise. Extensive experiments conducted on three SAR datasets verify the superior performance of the proposed CAMixer. The source codes will be publicly available at https://github.com/summitgao/CAMixer .

采用类Transformer的CAMixer结构结合全局注意力机制进行合成孔径雷达图像变化检测，通过并行设计将自注意力和平移卷积相结合，实现全局语义信息的捕获，并同时进行局部特征提取，并采用门控机制来增强非线性特征变换，进而提高对杂波噪声的高质量表示。经过在三个合成孔径雷达数据集上的广泛实验验证，CAMixer模型展现出卓越的性能。

合成孔径雷达图像变化检测卷积和注意力混合模型