ConvTransNet: A CNN–Transformer Network for Change Detection With Multiscale Global–Local Representations

Abstract

Change detection (CD) in optical remote sensing images has significantly benefited from the development of deep convolutional neural networks (CNNs) due to their strong capability of local modeling in bi-temporal images. In addition, the recent rise of transformer modules leads to the improvement of global feature extraction of bi-temporal remote sensing images. Note that the existing simple cascade of deep CNNs and transformer modules shows limited CD performance on small changed areas due to deficiencies of multi-scale information therein. To address the aforementioned issue, we propose a new CNN-transformer network (ConvTransNet) with multi-scale framework to better exploit global-local information in optical remote sensing images. In our ConvTransNet, we propose the parallel-branch ConvTrans block as the basic component to generate global-local features, i.e., adaptively integrates the global features summarized by a transformer-based branch and the local features extracted by a convolution-based branch, providing better identifiability between changed areas and unchanged areas. By fusing multiple global-local features with different scales, our ConvTransNet improves the robustness of the CD performance on changed areas with different sizes, especially small changed areas. Experiments on two public change detection datasets of optical remote sensing images, i.e., LEVIR-CD and CDD, demonstrate that our ConvTransNet achieves enhanced CD performance than the other commonly used methods.