A Multiscale Cascaded Cross-Attention Hierarchical Network for Change Detection on Bitemporal Remote Sensing Images

Remote sensing image change detection (RSCD) is an important task in remote sensing image interpretation. Some recent RSCD works focus on the extraction and interaction of global and local information. However, the current work underutilizes hierarchical features and may introduce noise from shallow encoders. In this paper, we propose a multi-scale cascaded cross-attention hierarchical network (MSCCA-Net). This network utilizes a large kernel convolution formed by stacking small kernel convolutions combined with Efficient Transformer as the backbone network to achieve local and global feature extraction and fusion. We proposed for the first time the idea of bottom-up level-by-level fusion of hierarchical features, based on which we designed the multi- scale cascade cross-attention (MSCCA) cross-fusion hierarchical features level by level from the bottom upwards, realizing the redistribution of spatial and semantic information, and thus enhancing the gainful effect of the skip connection mechanism in the field of RSCD. Our experiments on three public datasets show that MSCCA is able to efficiently perform the reorganization of hierarchical features thus avoiding misdetection and omission of small targets. Meanwhile, MSCCA-Net has more excellent comprehensive performance compared with other state-of-the-art methods.