HSI-Road Relabeled: Surface-Aware Road-Scene Segmentation

2026-09-10

AI Summary

本文通过引入六类标签和RGB-NIR配准方法，解决了HSI-Road数据集缺乏表面级别标签的问题，并评估了不同输入配置下的语义分割模型性能。

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.

Problem

• surface-level labels
• semantic-segmentation models
• RGB-to-NIR registration
• spatial-resolution
• NIR

Innovation

• surface-aware segmentation
• RGB-to-NIR registration
• multi-channel input
• semantic segmentation models