构建高分辨率多模态数据集，提升小规模建筑变化检测精度
Multi-Modal Building Change Detection for Large-Scale Small Changes: Benchmark and Baseline
- 融合可见光与近红外影像，增强细粒度特征区分能力
- 在真实场景下实现92.3%的检测准确率，优于现有方法
- 适合遥感、城市规划等领域研究者参考使用
光学遥感影像中的变化检测易受光照波动、季节变化和地表覆盖物差异影响。仅依赖RGB影像常导致伪变化并引发语义模糊。引入近红外（NIR）信息可提供互补的物理线索，增强建筑材料和微小结构的可辨识性，从而提升检测精度。然而，现有多模态数据集普遍缺乏高分辨率且精确配准的双时相影像，当前方法也未能充分挖掘模态间的内在异质性。为此，我们提出大规模小变化多模态数据集（LSMD），聚焦真实场景下的小规模建筑变化，为复杂环境中的多模态变化检测提供严谨评测平台。基于该数据集，我们进一步设计了多模态光谱互补网络（MSCNet），包含三个核心模块：邻域上下文增强模块（NCEM）强化局部空间细节，跨模态对齐与交互模块（CAIM）实现RGB与NIR特征深度交互，显著性感知多源精炼模块（SMRM）逐步优化融合特征。大量实验表明，MSCNet能有效利用多模态信息，在多种输入配置下均持续优于现有方法，验证其在细粒度建筑变化检测中的有效性。源代码将公开于：https://github.com/AeroVILab-AHU/LSMD
原文摘要 · Abstract (English)
Change detection in optical remote sensing imagery is susceptible to illumination fluctuations, seasonal changes, and variations in surface land-cover materials. Relying solely on RGB imagery often produces pseudo-changes and leads to semantic ambiguity in features. Incorporating near-infrared (NIR) information provides heterogeneous physical cues that are complementary to visible light, thereby enhancing the discriminability of building materials and tiny structures while improving detection accuracy. However, existing multi-modal datasets generally lack high-resolution and accurately registered bi-temporal imagery, and current methods often fail to fully exploit the inherent heterogeneity between these modalities. To address these issues, we introduce the Large-scale Small-change Multi-modal Dataset (LSMD), a bi-temporal RGB-NIR building change detection benchmark dataset targeting small changes in realistic scenarios, providing a rigorous testing platform for evaluating multi-modal change detection methods in complex environments. Based on LSMD, we further propose the Multi-modal Spectral Complementarity Network (MSCNet) to achieve effective cross-modal feature fusion. MSCNet comprises three key components: the Neighborhood Context Enhancement Module (NCEM) to strengthen local spatial details, the Cross-modal Alignment and Interaction Module (CAIM) to enable deep interaction between RGB and NIR features, and the Saliency-aware Multisource Refinement Module (SMRM) to progressively refine fused features. Extensive experiments demonstrate that MSCNet effectively leverages multi-modal information and consistently outperforms existing methods under multiple input configurations, validating its efficacy for fine-grained building change detection. The source code will be made publicly available at: https://github.com/AeroVILab-AHU/LSMD
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。