LG-CD: Enhancing Language-Guided Change Detection through SAM2 Adaptation

• URL: http://arxiv.org/abs/2509.21894v1
• Date: Fri, 26 Sep 2025 05:30:11 GMT
• Title: LG-CD: Enhancing Language-Guided Change Detection through SAM2 Adaptation
• Authors: Yixiao Liu, Yizhou Yang, Jinwen Li, Jun Tao, Ruoyu Li, Xiangkun Wang, Min Zhu, Junlong Cheng,
• Abstract summary: We propose a novel Language-Guided Change Detection model (LG-CD)<n>This model leverages natural language prompts to direct the network's attention to regions of interest.<n>Our experiments on three datasets demonstrate that LG-CD consistently outperforms state-of-the-art change detection methods.
• Score: 9.324344835427858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote Sensing Change Detection (RSCD) typically identifies changes in land cover or surface conditions by analyzing multi-temporal images. Currently, most deep learning-based methods primarily focus on learning unimodal visual information, while neglecting the rich semantic information provided by multimodal data such as text. To address this limitation, we propose a novel Language-Guided Change Detection model (LG-CD). This model leverages natural language prompts to direct the network's attention to regions of interest, significantly improving the accuracy and robustness of change detection. Specifically, LG-CD utilizes a visual foundational model (SAM2) as a feature extractor to capture multi-scale pyramid features from high-resolution to low-resolution across bi-temporal remote sensing images. Subsequently, multi-layer adapters are employed to fine-tune the model for downstream tasks, ensuring its effectiveness in remote sensing change detection. Additionally, we design a Text Fusion Attention Module (TFAM) to align visual and textual information, enabling the model to focus on target change regions using text prompts. Finally, a Vision-Semantic Fusion Decoder (V-SFD) is implemented, which deeply integrates visual and semantic information through a cross-attention mechanism to produce highly accurate change detection masks. Our experiments on three datasets (LEVIR-CD, WHU-CD, and SYSU-CD) demonstrate that LG-CD consistently outperforms state-of-the-art change detection methods. Furthermore, our approach provides new insights into achieving generalized change detection by leveraging multimodal information.

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