UAV-based hyperspectral images of seventeen red and white grapevine varieties

This dataset accompanies the journal article "Classification of Grapevine Varieties Using UAV Hyperspectral Imaging," published in Remote Sensing (MDPI): https://doi.org/10.3390/rs16122103. It contains hyperspectral images acquired by an unmanned aerial vehicle (UAV) covering seventeen grapevine varieties, grouped into red and white cultivars. The images are provided in their original, non-rectified form to reduce memory usage. Note that training was conducted on these unrectified images; the orthorectified mosaic for red varieties alone exceeds 70 GB and is not included due to size limits in Zenodo. Each hyperspectral image is accompanied by a header file (.hdr) and can be viewed and processed using SpectralView, which we highly recommend for both visualization and analysis. To facilitate quick inspection, an RGB extract is also provided for each hyperspectral image. Labeling was performed using the open-source tool Sensarea. NDVI images were generated and binarized to aid in segmentation. Rows of vines were then manually labeled within Sensarea based on these NDVI-derived masks. Code for loading and training on this dataset is available on GitHub.本数据集配套发表于《Remote Sensing》（MDPI出版社）的期刊论文《利用无人机高光谱成像技术分类葡萄品种（Classification of Grapevine Varieties Using UAV Hyperspectral Imaging）》，DOI链接为：https://doi.org/10.3390/rs16122103。 数据集包含由无人机（Unmanned Aerial Vehicle, UAV）采集的高光谱图像（hyperspectral images），覆盖17个葡萄品种，可分为红葡萄与白葡萄栽培品种两类。为减少内存占用，图像以原始未校正格式提供。需注意，模型训练即基于此类未校正图像；仅红葡萄品种的正射校正镶嵌图体积就超过70 GB，受限于Zenodo平台的存储空间限制，该内容未被纳入本数据集。 每张高光谱图像均附带一个头文件（.hdr），可通过SpectralView工具进行查看与处理，我们推荐使用该工具完成可视化与分析工作。为便于快速预览，每张高光谱图像还提供了对应的RGB提取版本。 数据集标注工作通过开源工具Sensarea完成：研究人员首先生成归一化差异植被指数（Normalized Difference Vegetation Index, NDVI）图像并进行二值化处理，以辅助图像分割；随后基于此类由NDVI生成的掩膜，在Sensarea中手动标注葡萄种植行。 用于加载本数据集并开展模型训练的代码已开源至GitHub平台。