A Dual-Branch Framework for Semantic Change Detection with Boundary and Temporal Awareness
This work addresses segmentation accuracy issues in remote sensing for applications like land-cover monitoring, but it appears incremental as it builds on existing methods like SAM and ResNet.
The paper tackled the problem of blurred boundaries and inadequate temporal modeling in semantic change detection from bi-temporal remote sensing images by proposing DBTANet, a dual-branch framework with boundary and temporal awareness, achieving state-of-the-art performance on two public benchmarks.
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.