The whiskbroom scanner is a critical component in remote sensing payloads, such as the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Joint Polar Satellite System (JPSS) and the Medium Resolution Spectral Imager (MERSI) on FengYun-3. However, panoramic distortion in whiskbroom scanner images increases overlap from the nadir to the edges between adjacent scans. These distortions present significant challenges for improving geolocation accuracy, particularly when errors occur in sensors and platforms. This manuscript derives analytic expressions for all potential error sources, including sensors, platforms, and elevation, using homogeneous coordinates in the focal plane. This derivation demonstrates that geolocation errors vary with view angles and detector positions. To further investigate these error properties, a gradient-aware least-squares matching method was developed to extract highly accurate and dense ground control points (GCPs) with approximately 100,000 points in a single scene. A three-step geometric calibration method was then introduced, which includes boresight misalignment correction, parametric geometric calibration, and non-uniform scanning compensation. Given the varying spatial resolution of the GCPs, the weight of the GCPs was dynamically updated for least-squares estimation. This method effectively demonstrated the complex geolocation errors in MERSI on FY-3D, a system that was not meticulously calibrated in the laboratory. The initial root mean square errors (RMSEs) were 3.354 and 12.441 instantaneous field of view (IFoV) for the designed parameters. The proposed geometric calibration method successfully corrected view-angle and detector position-related geolocation errors, reducing them to 0.211 and 0.225 IFoV in the scan and track directions, respectively. The geolocation validation software and experiment results were provided https://github.com/hongbop/whiskgeovalidation.git.

中文翻译：

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MERSI 扫帚扫描仪的在轨几何校准


扫帚扫描仪是遥感有效载荷的关键部件，例如联合极地卫星系统 （JPSS） 上的可见光红外成像辐射计套件 （VIIRS） 和风云三号上的中分辨率光谱成像仪 （MERSI）。但是，Whiskbroom 扫描仪图像中的全景失真会增加相邻扫描之间从像底点到边缘的重叠。这些变形对提高地理定位精度提出了重大挑战，尤其是在传感器和平台中发生错误时。本手稿使用焦平面中的齐次坐标推导出所有潜在误差源（包括传感器、平台和高程）的解析表达式。这种推导表明，地理位置误差随视角和探测器位置而变化。为了进一步研究这些误差特性，开发了一种梯度感知最小二乘匹配方法，以提取单个场景中大约有 100,000 个点的高精度和密集地面控制点 （GCP）。然后介绍了一种三步几何校准方法，包括视轴错位校正、参数化几何校准和非均匀扫描补偿。鉴于 GCP 的空间分辨率不同，GCP 的权重被动态更新以进行最小二乘估计。这种方法有效地证明了 MERSI 在 FY-3D 上的复杂地理定位误差，该系统在实验室中没有经过精心校准。设计参数的初始均方根误差 （RMSE） 分别为 3.354 和 12.441 瞬时视场 （IFoV）。所提出的几何校准方法成功地纠正了视角和探测器位置相关的地理定位误差，将它们分别降低到 0.211 和 0。扫描和航迹方向分别为 225 IFoV。https://github.com/hongbop/whiskgeovalidation.git 提供了地理定位验证软件和实验结果。