Asymmetric Cross-Attention Hierarchical Network Based on CNN and Transformer for Bitemporal Remote Sensing Images Change Detection

As an important task in the field of remote sensing (RS) image processing, RS image change detection (CD) has made significant advances through the use of convolutional neural networks (CNNs). The transformer has recently been introduced into the field of CD due to its excellent global perception capabilities. Some works have attempted to combine CNN and transformer to jointly harvest local-global features; however, these works have not paid much attention to the interaction between the features extracted by both. Also, the use of the transformer has resulted in significant resource consumption. In this article, we propose the Asymmetric Cross-attention Hierarchical Network (ACAHNet) by combining CNN and transformer in a series-parallel manner. The proposed Asymmetric Multiheaded Cross Attention (AMCA) module reduces the quadratic computational complexity of the transformer to linear, and the module enhances the interaction between features extracted from the CNN and the transformer. Different from the early and late fusion strategies employed in previous work, the effectiveness of the mid-term fusion strategy employed by ACAHNet shows a new choice of timing for feature fusion in the CD task. Our experiments on the proposed method on three public datasets show that our network has a better performance in terms of effectiveness and computational resource consumption compared to other comparative methods.