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.

翻译：基于深度学习的遥感图像变化检测（CD）近年来受到广泛关注。然而，如何有效提取并融合双时相图像的深层特征以提高CD精度仍是挑战。为此，本文提出一种新颖的基于三维卷积的邻层特征融合网络（命名为AFCF3D-Net）。首先，利用三维卷积的内在融合特性，设计了一种新型特征融合方式，可同时对双时相图像的特征信息进行提取与融合。其次，为缓解低层特征与高层特征之间的语义鸿沟，提出邻层特征交叉融合（AFCF）模块，用于聚合相邻层之间的互补特征信息。此外，引入全尺度跳跃连接策略，提升像素级预测能力及结果中变化目标的紧致性。最后，所提出的AFCF3D-Net在三个具有挑战性的遥感图像CD数据集上进行了验证：武汉建筑物数据集（WHU-CD）、LEVIR建筑物数据集（LEVIR-CD）和中山大学数据集（SYSU-CD）。定量分析与定性比较结果表明，与现有最优方法相比，所提出的AFCF3D-Net取得了更优性能。本工作代码可在https://github.com/wm-Githuber/AFCF3D-Net获取。