DMSHNet: Multiscale and Multisupervised Hierarchical Network for Remote-Sensing Image Change Detection

Change detection (CD) is an increasingly popular research direction in the field of remote sensing (RS). With the rapid development of RS, the resolution of RS images is gradually improving, which also puts forward higher requirements for the generalization ability of CD models. Due to its excellent feature extraction capabilities, convolutional neural networks (CNNs) have achieved great success in CD tasks in the past. However, CNN cannot model remote context, which also limits its performance on CD. In contrast, Transformer does well in modeling remote contextual information. To make an end, in order to make full use of the advantages of both, we fully combine CNN and Transformer and propose multiscale and multisupervised hierarchical network for RS Image CD, called DMSHNet. First, we introduce multiple convolution kernels of different sizes in the Siamese encoder to help DMSHNet perceive information of different scales. Second, we develop the change information enhancement module (CIEM), which mainly generates differential features at different layers. CIEM uses simple absolute value subtraction and atrous convolution to highlight changing information and suppress unchanged information in different ranges. Third, we propose multisupervised and multiscale cascade module (DMSCM), which integrates multilayer differential features generated by CIEM on the basis of fully considering local information, global information, and multiscale information. We have conducted sufficient experiments on four public datasets, and the experimental results show that our DMSHNet achieves excellent performance. Our source code is available at https://github.com/ahlpc/DMSHNet.git.