Hsi Road: A Hyper Spectral Image Dataset For Road Segmentation
Jiarou Lu, Huafeng Liu, Yazhou Yao, Shuyin Tao, Zhenmin Tang, Jianfeng Lu
Abstract
Road segmentation is a challenging task in the field of self-driving research. This paper present a road dataset built by hyper spectral imaging (HSI) cameras instead of the 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 annotation in both urban and rural scenes. It 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 released at github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">‡</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">‡</sup> https://github.com/NUST-Machine-Intelligence-Laboratory/hsi_road