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${}_{\text{ori}}$), registered low-resolution RGB (RGB${}_{\text{reg}}$), NIR, and channel-stacked RGB${}_{\text{reg}}$--NIR (RGBN${}_{\text{stk}}$). The comparison quantifies the effect of spatial-resolution reduction on RGB, along with evaluation of NIR and RGBN${}_{\text{stk}}$, with results reported using per-class and mean IoU and F1 scores. RGB${}_{\text{ori}}$ achieves the highest overall performance but contains 12$\times$ more pixels than the matched-resolution inputs. At the matched 192$\times$384 resolution, RGBN${}_{\text{stk}}$ outperforms NIR for all six SSMs and RGB${}_{\text{reg}}$ for five of six, with the most consistent gains for the Water class. These results highlight the importance of spatial resolution while showing that NIR provides complementary information to RGB.