arXiv:2409.06520cs.CV2024-09被引 1

仅用原始影像和低质量轨迹数据,实现轻型航摄高光谱相机的自动校正。

In Flight Boresight Rectification for Lightweight Airborne Pushbroom Imaging Spectrometry

  • 基于原始光谱影像与低质轨迹,自动提取匹配点并校准相机参数。
  • 在无精确地形与轨迹信息下,仍达到接近人工校准的精度。
  • 适用于无人机等轻型平台,对设备条件要求更低,适合实际应用。

近年来,高光谱相机已小型化,可部署于无人机或小型飞机等轻型航空平台。与帧式相机(如RGB或多光谱)不同,许多高光谱传感器采用线阵扫描('push-broom')设计,这给图像几何校正及内外参标定带来显著挑战。传统方法依赖高精度的GPS/INS轨迹估计与详细地形模型,但轨迹或地表模型误差会引入系统性偏差,影响几何建模精度,进而降低校正质量。为克服此问题,本文提出一种仅利用原始光谱影像与可能低质量的GPS/INS轨迹,实现推扫式高光谱传感器的同名点提取与相机标定的方法。实验表明,该方法可实现轻型航摄系统的全自动校正,优于现有先进自动校正方法,且精度媲美人工校准方案。

原文摘要 · Abstract (English)

Hyperspectral cameras have recently been miniaturized for operation on lightweight airborne platforms such as UAV or small aircraft. Unlike frame cameras (RGB or Multispectral), many hyperspectral sensors use a linear array or 'push-broom' scanning design. This design presents significant challenges for image rectification and the calibration of the intrinsic and extrinsic camera parameters. Typically, methods employed to address such tasks rely on a precise GPS/INS estimate of the airborne platform trajectory and a detailed terrain model. However, inaccuracies in the trajectory or surface model information can introduce systematic errors and complicate geometric modeling which ultimately degrade the quality of the rectification. To overcome these challenges, we propose a method for tie point extraction and camera calibration for 'push-broom' hyperspectral sensors using only the raw spectral imagery and raw, possibly low quality, GPS/INS trajectory. We demonstrate that our approach allows for the automatic calibration of airborne systems with hyperspectral cameras, outperforms other state-of-the-art automatic rectification methods and reaches an accuracy on par with manual calibration methods.

高光谱成像航摄校正无人机

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