A Dual-Branch Framework for Semantic Change Detection with Boundary and Temporal Awareness

• URL: http://arxiv.org/abs/2602.11466v1
• Date: Thu, 12 Feb 2026 00:54:22 GMT
• Title: A Dual-Branch Framework for Semantic Change Detection with Boundary and Temporal Awareness
• Authors: Yun-Cheng Li, Sen Lei, Heng-Chao Li, Ke Li,
• Abstract summary: We propose a Dual-Branch Framework for Semantic Change Detection with Boundary and Temporal Awareness, termed ANet.<n>ANet integrates global semantics, local details, temporal reasoning, and boundary awareness, achieving state-of-the-art performance.
• Score: 8.202209362704494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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