Australian Centre for Filed Robotics (ACFR), The University of Sydney, Sydney, NSW 2006, Australia.
Sensors (Basel). 2018 Jun 8;18(6):1885. doi: 10.3390/s18061885.
Hyperspectral line-scan cameras are increasingly being deployed on mobile platforms operating in unstructured environments. To generate geometrically accurate hyperspectral composites, the intrinsic parameters of these cameras must be resolved. This article describes a method for determining the intrinsic parameters of a hyperspectral line-scan camera. The proposed method is based on a cross-ratio invariant calibration routine and is able to estimate the focal length, principal point, and radial distortion parameters in a hyperspectral line-scan camera. Compared to previous methods that use similar calibration targets, our approach extends the camera model to include radial distortion. It is able to utilize calibration data recorded from multiple camera view angles by optimizing the re-projection error of all calibration data jointly. The proposed method also includes an additional signal processing step that automatically detects calibration points in hyperspectral imagery of the calibration target. These contributions result in accurate estimates of the intrinsic parameters with minimal supervision. The proposed method is validated through comprehensive simulation and demonstrated on real hyperspectral line-scans.
高光谱线扫描相机越来越多地部署在在非结构化环境中运行的移动平台上。为了生成具有精确几何形状的高光谱合成图像，必须确定这些相机的内部参数。本文描述了一种确定高光谱线扫描相机内部参数的方法。所提出的方法基于交比不变校准例程，能够估计高光谱线扫描相机的焦距、主点和径向失真参数。与使用类似校准目标的先前方法相比，我们的方法通过联合优化所有校准数据的重投影误差，将相机模型扩展到包括径向失真。它能够通过优化所有校准数据的重投影误差来利用从多个相机视角记录的校准数据。所提出的方法还包括一个附加的信号处理步骤，该步骤可以自动检测校准目标的高光谱图像中的校准点。这些贡献使得可以在最小监督的情况下准确估计内部参数。该方法通过全面的模拟进行了验证，并在真实的高光谱线扫描上进行了演示。
