msuav500k:用于多光谱和RGB无人驾驶飞行器图像的基础数据集
Jurrian Doornbos1, Önder Babur1,2
1Wageningen University and Research, Information Technology, Hollandseweg 1, 6706 KN Wageningen, The Netherlands.
Data in brief
|November 10, 2025
概括
这项研究提供了对校准无人机 (UAV) 多光谱图像的最大数据集,确保计算机视觉和遥感应用的放射性一致性.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 地理空间数据科学数据科学
背景情况:
- 无人机 (UAV) 图像对各种应用至关重要.
- 现有的数据集在各种传感器中往往缺乏放射性一致性.
- 对于先进的模型培训,需要标准化,高质量的多谱数据.
研究的目的:
- 策划和校准最大的公开可用的无人机多光谱图像数据集.
- 为了确保跨多种传感器类型的放射性一致性,以便进行交叉研究的可比性.
- 为培训和微调遥感和计算机视觉基础模型提供一个有价值的资源.
主要方法:
- 从开放访问平台对无人机RGB和多光谱图像进行系统的放射测量校准.
- 针对传感器特定变量的原始数字数字 (DN) 校正 (黑色水平,微图).
- 使用内部太阳光传感器实现像素值的规范化,以保持一致性.
主要成果:
- 该数据集包括最大的无人机图像集合,至少有四个光谱带 (绿色,红色,红边,近红外).
- 图像以UINT8 5通道多光谱TIFF和RGB 3通道JPG格式的orthomosaic或原始传感器数据提供.
- 全面的元数据,处理脚本和校准细节是公开可访问的.
结论:
- 该数据集为无人机多谱数据提供了一个独特的,放射性一致的资源.
- 促进了强大的交叉研究比较和基础模型的开发.
- 支持计算机视觉和遥感研究和实践的进步.
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