构建大规模高光谱遥感分类基准，支持细粒度地物识别与分割。
HyperImageNet: A Large-Scale High-Spatial Resolution Hyperspectral Imagery Classification Benchmark
- 采集2.6万张高光谱图像，含224波段与138类地物标签。
- 提供像素级语义标注与实例掩码，支持语义与实例分割任务。
- 适用于开放环境下的遥感模型评估与基础模型训练。
我们提出HyperImageNet，一个用于细粒度高光谱地物理解的大规模基准数据集。该数据集包含26,084个机载高光谱图像块，具有224个光谱波段和138种细粒度地物类别。与现有数据集不同，HyperImageNet提供原始影像、像素级语义标签和对象级实例掩码，支持语义分割与实例分割任务。此外，我们建立了严格的空域分离的开放环境评测基准，用于评估代表性方法及HyperFree基础模型。实验结果表明，HyperImageNet在细粒度高光谱理解与开放环境遥感研究中具有显著有效性。
原文摘要 · Abstract (English)
We present HyperImageNet, a large-scale benchmark for fine-grained hyperspectral land-cover understanding. The dataset contains 26,084 airborne hyperspectral image patches with 224 spectral bands and 138 fine-grained land-cover categories. Unlike existing datasets, HyperImageNet provides raw imagery, pixel-level semantic labels, and object-level instance masks, supporting both semantic and instance segmentation. Furthermore, we establish an open-environment benchmark with strict spatial separation to evaluate representative methods and the HyperFree foundation model. Experimental results demonstrate the effectiveness of HyperImageNet for fine-grained hyperspectral understanding and open-environment remote sensing research.
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