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 .

翻译：合成孔径雷达（SAR）图像变化检测是一项关键任务，近年来在遥感领域受到越来越多的关注。然而，现有的SAR变化检测方法主要基于卷积神经网络（CNN），对全局注意力机制的考虑有限。在本论文中，我们探索类似Transformer的架构用于SAR变化检测，以引入全局注意力。为此，我们提出了一种卷积与注意力混合器（CAMixer）。首先，为了补偿Transformer的归纳偏置，我们将自注意力与移位卷积以并行方式相结合。这种并行设计通过自注意力有效捕获全局语义信息，同时通过移位卷积进行局部特征提取。其次，我们在前馈网络中采用门控机制以增强非线性特征变换。门控机制被表述为两个并行线性层的逐元素乘法。重要特征得以突出，从而生成对抗散斑噪声的高质量表示。在三个SAR数据集上进行的广泛实验验证了所提出的CAMixer的优越性能。源代码将在https://github.com/summitgao/CAMixer公开。