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

TL;DR

This paper introduces DBTANet, a dual-branch framework that improves semantic change detection in remote sensing images by integrating global semantics, local details, temporal dependencies, and boundary awareness, achieving state-of-the-art results.

Contribution

The paper proposes a novel dual-branch Siamese encoder with boundary and temporal modules for enhanced semantic change detection in remote sensing images.

Findings

Achieves state-of-the-art performance on public benchmarks.

Effectively integrates global semantics and local details.

Enhances boundary accuracy through specialized modules.

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.