A CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change Detection

Change detection methods play an indispensable role in remote sensing. Some change detection methods have obtained a fairly good performance by introducing attention mechanism on the basis of the convolutional neural network (CNN), but identifying intricate changes remains difficult. In response to these problems, this article proposes a new model for detecting changes in remote sensing, namely, MTCNet, which combines the advantages of multi-scale Transformer with the convolutional block attention module (CBAM) to improve the detection quality of different remote sensing images. On the basis of traditional convolutions, the Transformer module is introduced to extract bitemporal image features by modeling contextual information. Based on the Transformer module, a multi-scale module is designed to form a multi-scale Transformer, which can obtain features at different scales in bitemporal images, thereby identifying the changes we are interested in. Based on the multiscale Transformer module, the CBAM is introduced. The CBAM is split into a spatial attention module (SAM) and a channel attention module (CAM), which are applied to the front and back ends of the multi-scale Transformer respectively. Spatial information and channel information of feature maps are modeled separately. In this article, the validity and efficiency of the method are verified by a large number of experiments on the LEVIR-CD dataset and the WHU-CD dataset.