MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images
This work addresses a specific issue in remote sensing change detection, offering an incremental improvement for applications like environmental monitoring.
The paper tackles the problem of degraded change detection performance on small changed areas in remote sensing images by proposing MCTNet, a multi-scale CNN-transformer network, which achieves better detection performance than existing state-of-the-art methods.
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.