HSI-Drive: A Dataset for the Research of Hyperspectral Image Processing Applied to Autonomous Driving Systems

Abstract

We present a structured dataset for the research and development of automated driving systems (ADS) sup- ported by hyperspectral imaging (HSI). The dataset contains per-pixel manually annotated images selected from videos recorded in real driving conditions that have been organized according to four environment parameters: season, daytime, road type, and weather conditions. The aim is to provide high data diversity and facilitate the automatic generation of data subsets for the evaluation of machine learning (ML) techniques applied to the research of ADS in different driving scenarios and environmental conditions. The video sequences have been captured with a small-size 25-band VNIR (Visible- NearInfraRed) snapshot hyperspectral camera mounted on a driving automobile. The current selection of classes for image annotation is aimed to provide reliable data for the spectral analysis of the items in the scenes; it is thus based on material surface reflectance patterns (spectral signatures). It is foreseen that future versions of the dataset will also incorporate alternative dense semantic labeling of the annotated images. The first version of the dataset, named HSI-Drive v1.0, is publicly available for download