论文标题
MCTNET：用于光学遥感图像中更改检测的多尺度CNN转换网络
MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images
论文作者
论文摘要
对于遥感图像中变更检测（CD）的任务，深度卷积神经网络（CNN）的方法最近汇总了变压器模块，以提高全局特征提取的能力。但是，由于深CNN和Transformer模块的简单单尺度整合，它们在较小的变化区域上遭受了降解的CD性能。为了解决这个问题，我们提出了一个基于多尺度CNN转换器结构的混合网络，称为MCTNET，其中利用了多尺度的全局和本地信息，以增强CD性能在变化的区域的稳健性，具有不同的尺寸。尤其是，我们设计了Convrans块，以适应来自变压器模块的全局特征和CNN层的本地特征，CNN层提供了具有不同尺度的丰富全球本地特征。实验结果表明，与现有的最新CD方法相比，我们的MCTNET实现更好的检测性能。
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.
