MutSimNet: Mutually Reinforcing Similarity Learning for RS Image Change Detection
Xu Liu, Yu Liu, Licheng Jiao, Lingling Li, Fang Liu, Shuyuan Yang, Biao Hou
Abstract
Change detection involves analysis of discrepancies between two phases. However, when the unchanged elements are known, the changed features to be identified become straightforward. In addition, remote sensing image is constrained by limited spectral information, which leads to blurred boundaries between different semantics. Based on these two prior knowledge, in this artical, we introduce a novel change detection framework, named the mutually reinforcing similarity network (MutSimNet). This architecture aims to minimize false alarms along changing boundaries and reduce misjudgment rates among outliers. First, similarity learning is applied to change detection. The relationship between the two phases is considered when deriving the change feature maps. Second, we devise a mutually reinforcing loss function that integrates initial features with final features. Third, a self-attention module is connected in the feature pyramid network. This design mitigates information loss during the down-sampling process. Fourth, an attention feature fusion strategy is proposed for the integration of multi-layer features. This strategy takes into account the interaction between layer-by-layer features. Fifth, experimental results validate MutSimNet’s efficiency, particularly its ability to focus on edge contour learning. The MutSimNet also achieves superior performance on two benchmark datasets and predicts positive samples with higher probability. The codebase is accessible at https://github.com/ly-yu/MutSimNet.