  Very-high-resolution (VHR) remote sensing (RS) image change detection (CD)
has been a challenging task for its very rich spatial information and sample
imbalance problem. In this paper, we have proposed a hierarchical change
guiding map network (HCGMNet) for change detection. The model uses hierarchical
convolution operations to extract multiscale features, continuously merges
multi-scale features layer by layer to improve the expression of global and
local information, and guides the model to gradually refine edge features and
comprehensive performance by a change guide module (CGM), which is a
self-attention with changing guide map. Extensive experiments on two CD
datasets show that the proposed HCGMNet architecture achieves better CD
performance than existing state-of-the-art (SOTA) CD methods.
