用大模型提升遥感变化检测，聚焦关键区域并加速收敛
SegChange-R1: LLM-Augmented Remote Sensing Change Detection
- 引入大语言模型描述文本，引导模型关注变化区域
- 在4个数据集上优于现有方法，提升显著
- 适配无人机视角，适合遥感与城市监测研究者
遥感变化检测通过分析同一区域不同时期的特征变化，广泛应用于城市规划、地形分析和环境监测。本文提出一种大语言模型增强的推理方法SegChange-R1，通过融合文本描述信息引导模型聚焦相关变化区域，加速收敛。设计基于线性注意力的空间变换模块（BEV），解决多模态特征对齐问题，将不同时间的特征统一到鸟瞰图空间。此外，构建了新数据集DVCD，用于无人机视角下的建筑物变化检测。在四个主流数据集上的实验表明，该方法显著优于现有方法。代码与预训练模型已开源。
原文摘要 · Abstract (English)
Remote sensing change detection is used in urban planning, terrain analysis, and environmental monitoring by analyzing feature changes in the same area over time. In this paper, we propose a large language model (LLM) augmented inference approach (SegChange-R1), which enhances the detection capability by integrating textual descriptive information and guides the model to focus on relevant change regions, accelerating convergence. We designed a linear attention-based spatial transformation module (BEV) to address modal misalignment by unifying features from different times into a BEV space. Furthermore, we introduce DVCD, a novel dataset for building change detection from UAV viewpoints. Experiments on four widely-used datasets demonstrate significant improvements over existing method The code and pre-trained models are available in {https://github.com/Yu-Zhouz/SegChange-R1}.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。