用深度学习方法来估计高分辨率卫星图像中的物体高度
Szymon Glinka1, Jarosław Bajer1, Damian Wierzbicki2
1Creotech Instruments S.A., 05-500 Piaseczno, Poland.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
这项研究引入了一个深度学习算法,通过分析阴影,自动估计单个高分辨率卫星图像的物体高度. 该方法在建筑物和风力轮机中实现了高精度,证明了其多功能性.
科学领域:
- 遥感 遥感 遥感 遥感
- 这是一种摄影计量技术 (photogrammetry).
- 计算机视觉 计算机视觉
背景情况:
- 从单个高分辨率卫星图像中提取详细信息是具有挑战性的,因为光谱异质性.
- 传统的图像处理方法不足以从复杂的城市景观中准确地提取特征.
研究的目的:
- 开发一种通用,完全自动化的算法,用高分辨率的光学卫星数据来估计物体的高度.
- 通过使用深度学习来克服经典方法的局限性,用于影子分析.
主要方法:
- 利用深度学习算法在卫星和空中图像中自动检测物体和影子.
- 通过分析阴影长度,并结合太阳高度和卫星光速角度等元数据来计算物体高度.
- 验证了算法的性能与波兰华沙的LiDAR数据对比.
主要成果:
- 在几十万个物体的高度估计中达到±4.66m的全球精度.
- 证明了该算法能够准确测量典型 (建筑物) 和非典型 (风力轮机) 对象的高度.
- 提出了一套用于对象-影子关系的代分析的算法.
结论:
- 基于深度学习的影子分析对于从单个卫星图像中自动估计高度是有效的.
- 开发的方法为城市景观分析和物体库存提供了多功能解决方案.
- 该算法显示了对于需要从遥感数据中获得精确3D信息的应用程序的巨大潜力.
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