Symmetry, Free Full-Text

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Remote sensing (RS) image change detection (CD) is the procedure of detecting the change regions that occur in the same area in different time periods. A lot of research has extracted deep features and fused multi-scale features by convolutional neural networks and attention mechanisms to achieve better CD performance, but these methods do not result in well-fused feature pairs of the same scale and features of different layers. To solve this problem, a novel CD network with symmetric structure called the channel-level hierarchical feature fusion network (CLHF-Net) is proposed. First, a channel-split feature fusion module (CSFM) with symmetric structure is proposed, which consists of three branches. The CSFM integrates feature information of the same scale feature pairs more adequately and effectively solves the problem of insufficient communication between feature pairs. Second, an interaction guidance fusion module (IGFM) is designed to fuse the feature information of different layers more effectively. IGFM introduces the detailed information from shallow features into deep features and deep semantic information into shallow features, and the fused features have more complete feature information of change regions and clearer edge information. Compared with other methods, CLHF-Net improves the F1 scores by 1.03%, 2.50%, and 3.03% on the three publicly available benchmark datasets: season-varying, WHU-CD, and LEVIR-CD datasets, respectively. Experimental results show that the performance of the proposed CLHF-Net is better than other comparative methods.