SAAN: Similarity-aware attention flow network for change detection with
  VHR remote sensing images
Haonan Guo Xin Su Chen Wu Bo Du Liangpei Zhang
Change detection (CD) is a fundamental and important task for monitoring the
land surface dynamics in the earth observation field. Existing deep
learning-based CD methods typically extract bi-temporal image features using a
weight-sharing Siamese encoder network and identify change regions using a
decoder network. These CD methods, however, still perform far from
satisfactorily as we observe that 1) deep encoder layers focus on irrelevant
background regions and 2) the models' confidence in the change regions is
inconsistent at different decoder stages. The first problem is because deep
encoder layers cannot effectively learn from imbalanced change categories using
the sole output supervision, while the second problem is attributed to the lack
of explicit semantic consistency preservation. To address these issues, we
design a novel similarity-aware attention flow network (SAAN). SAAN
incorporates a similarity-guided attention flow module with deeply supervised
similarity optimization to achieve effective change detection. Specifically, we
counter the first issue by explicitly guiding deep encoder layers to discover
semantic relations from bi-temporal input images using deeply supervised
similarity optimization. The extracted features are optimized to be
semantically similar in the unchanged regions and dissimilar in the changing
regions. The second drawback can be alleviated by the proposed
similarity-guided attention flow module, which incorporates similarity-guided
attention modules and attention flow mechanisms to guide the model to focus on
discriminative channels and regions. We evaluated the effectiveness and
generalization ability of the proposed method by conducting experiments on a
wide range of CD tasks. The experimental results demonstrate that our method
achieves excellent performance on several CD tasks, with discriminative
features and semantic consistency preserved.