OHID-1: A New Large Hyperspectral Image Dataset for Multi-Classification

In the context of the increasing popularity of Big Data paradigms and deep learning techniques, we introduce a novel large-scale hyperspectral imagery dataset, termed Orbita Hyperspectral Images Dataset-1 (OHID-1). It comprises 10 hyperspectral images sourced from diverse regions of Zhuhai City, China, each boasting 32 spectral bands with a spatial resolution of 10 meters and spanning a spectral range of 400-1000 nanometers. The core objective of this dataset is to elevate the performance of hyperspectral image classification and pose substantial challenges to existing hyperspectral image processing algorithms. When compared to traditional open-source hyperspectral datasets and recently released large-scale hyperspectral datasets, OHID-1 presents more intricate features and a higher degree of classification complexity by providing 7 classes labels in wider area. Furthermore, this study demonstrates the utility of OHID-1 by testing it with selected hyperspectral classification algorithms. This dataset will be useful to advance cutting-edge research in urban sustainable development science, land use analysis. We invite the scientific community to devise novel methodologies for an in-depth analysis of these data.

随着大数据范式与深度学习技术的日益普及，我们提出一款新型大规模高光谱图像数据集，命名为Orbita高光谱图像数据集-1（Orbita Hyperspectral Images Dataset-1，简称OHID-1）。该数据集包含取自中国珠海市不同区域的10幅高光谱图像，每幅图像均具备32个光谱波段，空间分辨率为10米，光谱覆盖范围为400~1000纳米。本数据集的核心目标在于提升高光谱图像分类任务的性能，并为现有高光谱图像处理算法带来显著挑战。相较于传统开源高光谱数据集与近期发布的大规模高光谱数据集，OHID-1通过覆盖更广阔的区域并提供7个类别标签，具备更复杂的特征与更高的分类复杂度。此外，本研究通过选用典型高光谱分类算法对OHID-1进行测试，验证了该数据集的应用价值。该数据集将助力推动城市可持续发展科学、土地利用分析领域的前沿研究。我们诚邀全球科研工作者针对该数据集开发全新分析方法，以开展深入研究。