HyperLeaf2024 - A Hyperspectral Imaging Dataset for Classification and Regression of Wheat Leaves

William Michael Laprade, Pawel Pieta, Svetlana Kutuzova, Jesper Cairo Westergaard, Mads Nielsen, Svend Christensen, Anders Bjorholm Dahl; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 1234-1243

Hyperspectral imaging is a widely used method in remote sensing particularly for use in airborne and satellite-based land surveillance. Its versatility is however much larger and has also seen usage in everything ranging from food processing and surveillance to astronomy and waste sorting. It is also gaining inroads with agricultural research. With most available datasets focusing on per-pixel classification there is however a potential for hyperspectral whole-image analysis but there is a severe lack of datasets for whole-image analysis. To help fill this gap and facilitate methodological development in whole-image hyperspectral image analysis we introduce the HyperLeaf2024 dataset. The dataset consists of 2410 hyperspectral images of wheat leaves along with associated classification and regression targets at both the leaf level and the plot level. In addition to the dataset we also provide experiments showing the importance of pretraining and highlighting the future research direction in whole-image hyperspectral image analysis.