Multiscale Attention Network Guided With Change Gradient Image for Land Cover Change Detection Using Remote Sensing Images

Learning performance is unsatisfactory when training deep-learning networks without prior-knowledge guidance. In this paper, a multi-scale change detection neural network guided by a change gradient image (CGI) was proposed. First, a multi-scale information attentional module was embedded in the backbone of UNet to achieve a multi-scale information fusion task of bi-temporal images. Second, the position channel attention module was promoted to make the neural network pay more attention to the spectral and spatial information in the multi-scale fused feature map. Finally, a change gradient guide module was proposed to optimize backpropagation and overcome the negative effects of pseudo-change. Compared with seven state-of-the-art methods using three pairs of real remote sensing images, the proposed approach could smoothen the salt-and-pepper noise from the detection maps and improve the detection accuracy. The quantitative improvements are about 1.67% and 3.00% in terms of overall accuracy and Kappa coefficient, respectively, thus confirming the feasibility and superiority of the proposed approach for detecting land cover change with remotely sensed images. Code: https://github.com/ImgSciGroup/MACGGNet.git.