  Change detection, as a research hotspot in the field of remote sensing, has
witnessed continuous development and progress. However, the discrimination of
boundary details remains a significant bottleneck due to the complexity of
surrounding elements between change areas and backgrounds. Discriminating the
boundaries of large change areas results in misalignment, while connecting
boundaries occurs for small change targets. To address the above issues, a
novel network based on the localization-then-refinement strategy is proposed in
this paper, namely LRNet. LRNet consists of two stages: localization and
refinement. In the localization stage, a three-branch encoder simultaneously
extracts original image features and their differential features for
interactive localization of the position of each change area. To minimize
information loss during feature extraction, learnable optimal pooling (LOP) is
proposed to replace the widely used max-pooling. Additionally, this process is
trainable and contributes to the overall optimization of the network. To
effectively interact features from different branches and accurately locate
change areas of various sizes, change alignment attention (C2A) and
hierarchical change alignment module (HCA) are proposed. In the refinement
stage, the localization results from the localization stage are corrected by
constraining the change areas and change edges through the edge-area alignment
module (E2A). Subsequently, the decoder, combined with the difference features
strengthened by C2A in the localization phase, refines change areas of
different sizes, ultimately achieving accurate boundary discrimination of
change areas. The proposed LRNet outperforms 13 other state-of-the-art methods
in terms of comprehensive evaluation metrics and provides the most precise
boundary discrimination results on the LEVIR-CD and WHU-CD datasets.
