Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection

• URL: http://arxiv.org/abs/2302.05109v2
• Date: Wed, 17 Jan 2024 05:39:34 GMT
• Title: Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection
• Authors: Yuanxin Ye, Mengmeng Wang, Liang Zhou, Guangyang Lei, Jianwei Fan, and Yao Qin
• Abstract summary: We propose a novel adjacent-level feature fusion network with 3D convolution (named AFCF3D-Net) The proposed AFCF3D-Net has been validated on the three challenging remote sensing CD datasets.
• Score: 20.776673215108815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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