HSI-Road重标注：表面感知的道路场景分割
原标题：HSI-Road Relabeled: Surface-Aware Road-Scene Segmentation
AI 摘要
该论文对HSI-Road数据集进行了重新标注，将原本的二分类（背景/道路）扩展为六类路面材质分类：背景、沥青、混凝土、泥土、水和草地，并提供了RGB到NIR的配准流程。研究在四种输入配置（原始RGB、配准低分辨率RGB、NIR、RGB-NIR通道堆叠）下评估了六种语义分割模型，使用逐类和平均IoU及F1分数进行报告。结果显示，在匹配的192×384分辨率下，RGBN在全部六个模型上优于NIR，并在五个模型上优于RGB，其中Water类增益最一致，表明NIR提供了与RGB互补的信息。
正文节选
HSI-Road Relabeled: Surface-Aware Road-Scene Segmentation Abstract The HSI-Road dataset provides paired RGB and 25-channel NIR (600–960 nm) images with binary masks but no surface-level labels. This paper introduces a manually labeled six-class taxonomy: Background, Asphalt, Concrete, Dirt, Water, and Grass, and an RGB-to-NIR registration pipeline with corresponding annotations. Six semantic-segmentation models (SSMs) are evaluated under four input configurations: original-resolution RGB (RGB),