Deep learning-based change detection 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 to improve the accuracy of change detection 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, in order to bridge 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 densely skip connection strategy is introduced to improve the capability of pixel-wise prediction and compactness of changed objects in the results. Finally, the proposed AFCF3D-Net has been validated on the three challenging remote sensing change detection datasets: Wuhan building dataset (WHU-CD), LEVIR building dataset (LEVIR-CD), and Sun Yat-Sen University (SYSU-CD). The results of quantitative analysis and qualitative comparison demonstrate that the proposed AFCF3D-Net achieves better performance compared to the other state-of-the-art change detection methods.

翻译：基于深度学习的遥感图像变化检测近年来受到越来越多的关注。然而，如何有效提取并融合双时相图像的深层特征以提高变化检测精度仍是一个挑战。为此，本文提出了一种新颖的基于3D卷积的邻层特征融合网络（命名为AFCF3D-Net）。首先，利用3D卷积的内部融合特性，我们设计了一种新的特征融合方式，能够同时提取并融合来自双时相图像的特征信息。然后，为弥合低层特征与高层特征之间的语义鸿沟，我们提出了一种邻层特征交叉融合（AFCF）模块，以聚合相邻层级间的互补特征信息。此外，引入密集跳跃连接策略，以提升逐像素预测能力并增强结果中变化目标的紧凑性。最后，在三个具有挑战性的遥感变化检测数据集上验证了所提出的AFCF3D-Net：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，与其他先进的变化检测方法相比，所提出的AFCF3D-Net取得了更优的性能。