  Deep learning-based change detection (CD) using remote sensing images has
received increasing attention in recent years. However, how to effectively
extract and fuse the deep features of bi-temporal images for improving the
accuracy of CD is still a challenge. To address that, a novel adjacent-level
feature fusion network with 3D convolution (named AFCF3D-Net) is proposed in
this article. First, through the inner fusion property of 3D convolution, we
design a new feature fusion way that can simultaneously extract and fuse the
feature information from bi-temporal images. Then, to alleviate the semantic
gap between low-level features and high-level features, we propose an
adjacent-level feature cross-fusion (AFCF) module to aggregate complementary
feature information between the adjacent levels. Furthermore, the full-scale
skip connection strategy is introduced to improve the capability of pixel-wise
prediction and the compactness of changed objects in the results. Finally, the
proposed AFCF3D-Net has been validated on the three challenging remote sensing
CD datasets: the Wuhan building dataset (WHU-CD), the LEVIR building dataset
(LEVIR-CD), and the Sun Yat-Sen University dataset (SYSU-CD). The results of
quantitative analysis and qualitative comparison demonstrate that the proposed
AFCF3D-Net achieves better performance compared to other state-of-the-art
methods. The code for this work is available at
https://github.com/wm-Githuber/AFCF3D-Net.
