Self-structured pyramid network with parallel spatial-channel attention for change detection in VHR remote sensed imagery

Land cover change detection (CD) in very-high-resolution (VHR) images is still impeded by weak pattern separability and land cover complexity. To address these challenges, a self-structured pyramid network (S 2 PNet) with a parallel spatial-channel attention mechanism (PSAM) and a self-structured feature pyramid (SFP) is proposed for a finer annotation of changed land cover. The proposed PSAM refines the features of different levels in dual-branch coordinated by running parallel without mutual influence for a better recognition of varied objects, which can lead to less incorrectly detected land cover. And the SFP integrates the embedded multi-scale features to acquire an improved cognition over multi-scale objects, which can contribute to a more complete annotation over diverse objects. All-round experiments over several widely used open large-scale VHR CD data sets are carried out, which indicate the efficiency and effectiveness of the proposed method. Related comparisons suggest that the proposed method can achieve higher performance over several existing state-of-the-art CD methods. The source codes will be released at https://github.com/HaiXing- 1998/S2PNet- CD .

Automated summary

This study proposes a self-structured pyramid network (S2PNet) with a parallel spatial-channel attention mechanism (PSAM) and a self-structured feature pyramid (SFP) for finer annotation of changed land cover in very-high-resolution (VHR) images. Experimental results demonstrate the efficiency and effectiveness of the proposed method on widely used VHR CD datasets, and show that it outperforms several existing state-of-the-art CD methods.