Structure-Aware Multiscale Hybrid Network for Change Detection of Remote Sensing Images

In recent years, convolutional neural network (CNN), with its powerful feature representation capabilities, has made remarkable achievements in the change detection of remote sensing images. However, CNN has shortcomings in modeling the long-range dependencies of dual-temporal images, resulting in the poor recognition of structural information. In contrast, the Transformer technology can effectively capture the long-distance dependencies between input pixels, thereby helping in perceiving and reasoning structural information in images. To solve the problem that existing change detection methods cannot consider global and local feature information in the model, a multiscale cascaded CNN-Transformer hybrid network was proposed in this study. This algorithm can completely use the global and local semantic information on a hybrid network and improve the ability of the model to perceive changes in object structures and semantic information. The cascade network enhances the interaction ability between various scales, making it easier for the model to understand the differences and connections between features with different granularities. In addition, in this study, feature weights were adjusted at various scales to improve the ability of the model to use multiscale information. The F1-score of the proposed method reaches 97.8% and 87.1% on the CDD and GZ-CD datasets, respectively. Experimental results on the two standard datasets show that this method can effectively use feature information with various scales to improve the change detection accuracy of the model.