  The rapid advancement of automated artificial intelligence algorithms and
remote sensing instruments has benefited change detection (CD) tasks. However,
there is still a lot of space to study for precise detection, especially the
edge integrity and internal holes phenomenon of change features. In order to
solve these problems, we design the Change Guiding Network (CGNet), to tackle
the insufficient expression problem of change features in the conventional
U-Net structure adopted in previous methods, which causes inaccurate edge
detection and internal holes. Change maps from deep features with rich semantic
information are generated and used as prior information to guide multi-scale
feature fusion, which can improve the expression ability of change features.
Meanwhile, we propose a self-attention module named Change Guide Module (CGM),
which can effectively capture the long-distance dependency among pixels and
effectively overcome the problem of the insufficient receptive field of
traditional convolutional neural networks. On four major CD datasets, we verify
the usefulness and efficiency of the CGNet, and a large number of experiments
and ablation studies demonstrate the effectiveness of CGNet. We're going to
open-source our code at https://github.com/ChengxiHAN/CGNet-CD.
