Change detection is a key task in Earth observation applications. Recently, deep learning methods have demonstrated strong performance and widespread application. However, change detection faces data scarcity due to the labor-intensive process of accurately aligning remote sensing images of the same area, which limits the performance of deep learning algorithms. To address the data scarcity issue, we develop a fine-tuning strategy called the Semantic Change Network (SCN). We initially pre-train the model on single-temporal supervised tasks to acquire prior knowledge of instance feature extraction. The model then employs a shared-weight Siamese architecture and extended Temporal Fusion Module (TFM) to preserve this prior knowledge and is fine-tuned on change detection tasks. The learned semantics for identifying all instances is changed to focus on identifying only the changes. Meanwhile, we observe that the locations of changes between the two images are spatially identical, a concept we refer to as spatial consistency. We introduce this inductive bias through an attention map that is generated by large-kernel convolutions and applied to the features from both time points. This enhances the modeling of multi-scale changes and helps capture underlying relationships in change detection semantics. We develop a binary change detection model utilizing these two strategies. The model is validated against state-of-the-art methods on six datasets, surpassing all benchmark methods and achieving F1 scores of 92.87%, 86.43%, 68.95%, 97.62%, 84.58%, and 93.20% on the LEVIR-CD, LEVIR-CD+, S2Looking, CDD, SYSU-CD, and WHU-CD datasets, respectively.

翻译：变化检测是地球观测应用中的一项关键任务。近年来，深度学习方法已展现出强大的性能与广泛的应用前景。然而，由于对同一区域遥感影像进行精确配准的过程需要大量人工，变化检测面临数据稀缺问题，这限制了深度学习算法的性能。为解决数据稀缺问题，我们开发了一种称为语义变化网络（SCN）的微调策略。我们首先在单时相监督任务上对模型进行预训练，以获取实例特征提取的先验知识。随后，模型采用共享权重的孪生网络架构和扩展的时序融合模块（TFM）来保持该先验知识，并在变化检测任务上进行微调。模型将原本用于识别所有实例的语义学习目标，转变为专注于识别仅发生变化的区域。同时，我们观察到两幅影像中变化区域的空间位置具有一致性，这一概念我们称之为空间一致性。我们通过由大核卷积生成并应用于双时相特征的注意力图来引入这一归纳偏置。这增强了对多尺度变化的建模能力，并有助于捕捉变化检测语义中的潜在关联。基于这两种策略，我们构建了一个二元变化检测模型。该模型在六个数据集上进行了与先进方法的对比验证，其性能超越了所有基准方法，在LEVIR-CD、LEVIR-CD+、S2Looking、CDD、SYSU-CD和WHU-CD数据集上分别取得了92.87%、86.43%、68.95%、97.62%、84.58%和93.20%的F1分数。