[Submitted on 10 Sep 2026]
Title:HSI-Road Relabeled: Surface-Aware Road-Scene Segmentation
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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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.12151 [cs.CV]
(or arXiv:2609.12151v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.12151
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Imad Ali Shah [view email] [v1] Thu, 10 Sep 2026 19:38:25 UTC (109 KB)
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