提出双分支框架，精准检测遥感影像地物变化边界与时间动态。
A Dual-Branch Framework for Semantic Change Detection with Boundary and Temporal Awareness
- 双分支结构融合全局语义与局部细节特征
- 对称时序模块捕捉多尺度时间依赖性
- 适合高精度遥感变化检测任务的科研与应用
语义变化检测（SCD）旨在从双时相遥感图像中检测并分类地表覆盖变化。现有方法常因边界模糊和时序建模不足导致分割精度受限。为此，本文提出具有边界与时间感知能力的双分支框架DBTANet。通过双分支孪生编码器，冻结的SAM分支捕获全局语义与边界先验，而ResNet34分支提供局部空间细节，实现互补特征表达。在此基础上，设计双向时序感知模块（BTAM），以对称方式聚合多尺度特征并捕捉时序依赖关系。此外，高斯平滑投影模块（GSPM）优化浅层SAM特征，在抑制噪声的同时增强边缘信息，实现边界感知约束。在两个公开基准上的大量实验表明，DBTANet有效融合全局语义、局部细节、时序推理与边界感知，达到当前最优性能。
原文摘要 · Abstract (English)
Semantic Change Detection (SCD) aims to detect and categorize land-cover changes from bi-temporal remote sensing images. Existing methods often suffer from blurred boundaries and inadequate temporal modeling, limiting segmentation accuracy. To address these issues, we propose a Dual-Branch Framework for Semantic Change Detection with Boundary and Temporal Awareness, termed DBTANet. Specifically, we utilize a dual-branch Siamese encoder where a frozen SAM branch captures global semantic context and boundary priors, while a ResNet34 branch provides local spatial details, ensuring complementary feature representations. On this basis, we design a Bidirectional Temporal Awareness Module (BTAM) to aggregate multi-scale features and capture temporal dependencies in a symmetric manner. Furthermore, a Gaussian-smoothed Projection Module (GSPM) refines shallow SAM features, suppressing noise while enhancing edge information for boundary-aware constraints. Extensive experiments on two public benchmarks demonstrate that DBTANet effectively integrates global semantics, local details, temporal reasoning, and boundary awareness, achieving state-of-the-art performance.
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