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Change detection (CD) is a fundamental and important task for monitoring theland surface dynamics in the earth observation field. Existing deeplearning-based CD methods typically extract bi-temporal image features using aweight-sharing Siamese encoder network and identify change regions using adecoder network. These CD methods, however, still perform far fromsatisfactorily as we observe that 1) deep encoder layers focus on irrelevantbackground regions and 2) the models' confidence in the change regions isinconsistent at different decoder stages. The first problem is because deepencoder layers cannot effectively learn from imbalanced change categories usingthe sole output supervision, while the second problem is attributed to the lackof explicit semantic consistency preservation. To address these issues, wedesign a novel similarity-aware attention flow network (SAAN). SAANincorporates a similarity-guided attention flow module with deeply supervisedsimilarity optimization to achieve effective change detection. Specifically, wecounter the first issue by explicitly guiding deep encoder layers to discoversemantic relations from bi-temporal input images using deeply supervisedsimilarity optimization. The extracted features are optimized to besemantically similar in the unchanged regions and dissimilar in the changingregions. The second drawback can be alleviated by the proposedsimilarity-guided attention flow module, which incorporates similarity-guidedattention modules and attention flow mechanisms to guide the model to focus ondiscriminative channels and regions. We evaluated the effectiveness andgeneralization ability of the proposed method by conducting experiments on awide range of CD tasks. The experimental results demonstrate that our methodachieves excellent performance on several CD tasks, with discriminativefeatures and semantic consistency preserved.

SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images