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.

翻译：变化检测(CD)是地球观测领域监测地表动态变化的基础且重要的任务。现有基于深度学习的CD方法通常采用权值共享的孪生编码网络提取双时相图像特征，并通过解码网络识别变化区域。然而，这些CD方法仍远未达到令人满意的性能，我们观察到以下两点：1)深层编码层聚焦于无关背景区域；2)模型在变化区域的置信度在不同解码阶段不一致。第一个问题源于深层编码层仅通过输出监督无法有效从不平衡的变化类别中学习，第二个问题则归因于显式语义一致性保持的缺失。为解决这些问题，我们设计了一种新型相似性感知注意力流网络(SAAN)。SAAN通过深度监督相似性优化集成相似性引导注意力流模块，实现高效变化检测。具体而言，我们采用深度监督相似性优化方法显式引导深层编码层从双时相输入图像中挖掘语义关联，从而应对第一个问题：优化后的特征在未变化区域具有语义相似性，而在变化区域呈现语义差异性。针对第二个缺陷，我们提出相似性引导注意力流模块，该模块融合了相似性引导注意力机制与注意力流机制，引导模型聚焦于判别性通道和区域。通过在多类CD任务上的实验验证，该方法具有良好的有效性与泛化能力。实验结果表明，本方法在保持判别性特征与语义一致性的前提下，在多项CD任务中均取得了优异性能。