For the task of change detection (CD) in remote sensing images, deep convolution neural networks (CNNs)-based methods have recently aggregated transformer modules to improve the capability of global feature extraction. However, they suffer degraded CD performance on small changed areas due to the simple single-scale integration of deep CNNs and transformer modules. To address this issue, we propose a hybrid network based on multi-scale CNN-transformer structure, termed MCTNet, where the multi-scale global and local information are exploited to enhance the robustness of the CD performance on changed areas with different sizes. Especially, we design the ConvTrans block to adaptively aggregate global features from transformer modules and local features from CNN layers, which provides abundant global-local features with different scales. Experimental results demonstrate that our MCTNet achieves better detection performance than existing state-of-the-art CD methods.

翻译：针对遥感图像变化检测任务，基于深度卷积神经网络的方法近期通过集成Transformer模块提升了全局特征提取能力。然而，由于深度CNN与Transformer模块仅进行简单的单尺度融合，导致它们在小变化区域上的变化检测性能下降。为解决这一问题，我们提出了一种基于多尺度CNN-Transformer结构的混合网络，称为MCTNet，该网络利用多尺度全局与局部信息增强对不同尺寸变化区域的变化检测鲁棒性。特别是，我们设计了ConvTrans块，用于自适应地聚合来自Transformer模块的全局特征和来自CNN层的局部特征，从而提供丰富的多尺度全局-局部特征。实验结果表明，我们的MCTNet在检测性能上优于现有最先进的变化检测方法。