HSI Road: A Hyper Spectral Image Dataset for Road Segmentation

Road segmentation is a challenging task in the field of unmanned ground vehicle(UGV) navigation. For UGV, cameras are necessary sensors for environmental understanding. In this paper, we present a road dataset built by hyperspectral imaging (HSI) cameras instead of widely-used RGB cameras. HSI image is informative in spectrums and full of potential for natural environment perception. In this article, a first-of-its-kind HSI road segmentation dataset is built with careful calibration and annotation in both urban and rural scenes. The dataset contains 3799 scenes with RGB and NIR bands as well as their respective masks. Unlike many existing datasets that provide urban scenes in RGB images only, our dataset expands the sensing spectrum to 28 bands and includes various kinds of road surfaces, such as asphalt, cement, dirt, and sand, under rural and natural scenes. We also provide benchmark performances based on the recently popular segmentation algorithms on this dataset. The dataset is going to open source as soon as possible.