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The paper introduces DBTANet, a dual-branch framework for semantic change detection in remote sensing images that aims to improve segmentation accuracy by addressing blurred boundaries and inadequate temporal modeling. DBTANet employs a Siamese encoder with a frozen SAM branch for global context and a ResNet34 branch for local details, along with a Bidirectional Temporal Awareness Module (BTAM) for temporal dependency modeling and a Gaussian-smoothed Projection Module (GSPM) for boundary refinement. Experiments on public benchmarks demonstrate state-of-the-art performance, indicating the effectiveness of integrating global semantics, local details, temporal reasoning, and boundary awareness.
Achieve state-of-the-art semantic change detection by fusing a frozen SAM branch for global context with a ResNet34 branch for local details, plus temporal and boundary awareness modules.
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.
