Star Tracker Geometric Calibration Through Full-State Estimation Including Attitude

A star tracker calibration method using star images is presented in this paper. Unlike previous works, the proposed method estimates all parameters and the attitudes at once in a single least-squares formulation for the optimal calibration, which can be easily converted to a recursive estimation form. In addition, this paper presents a method to estimate the overall star tracker performance for attitude determination from the calibration results. Since the proposed method uses star images only, it can be applied to both on-orbit and ground star tracker calibration. The simulations show improvements in calibration performance about four times compared to the previous calibration method. The calibration experiments with actual star images are conducted to test its application.

A geometric calibration approach for industrial cone-beam CT system based on low-rank phantom

Precise alignment of the system scan geometry is crucial to ensure the reconstructed image quality in the cone-beam CT system. A calibration method that depends on the local feature of ball bearings phantom and point-like markers is probably affected by local image variations. Besides, multiple projections with circular scanning are usually required by this type of method to derive misaligned parameters. In contrast to previous works, this paper proposes a method that depends on the global symmetric low-rank feature of a novel phantom, which can accurately represent the system geometrical misalignment. All the misaligned parameters of the cone-beam CT system can be estimated from a single perspective direction without circular scanning. Meanwhile, since the global low-rank feature of the phantom is utilized, the proposed method is robust to the noise. Extensive simulations and real experiments validate the accuracy and robustness of our method, which achieves better performance compared to an existing phantom-based method.

Accuracy Evaluation on Geolocation of the Chinese First Polar Microsatellite (Ice Pathfinder) Imagery

Ice Pathfinder (Code: BNU-1), launched on September 12, 2019, is the first Chinese polar observation microsatellite. Its main payload is a wide-view camera with a ground resolution of 74 m at the subsatellite point and a scanning width of 744 km. BNU-1 takes into account the balance between spatial resolution and revisit frequency, providing observations with finer spatial resolution than Terra/Aqua MODIS data and more frequent revisits than Landsat-8 OLI and Sentinel-2 MSI. It is a valuable supplement for polar observations. Geolocation is an essential step in satellite image processing. This study aims to geolocate BNU-1 images; this includes two steps. For the first step, a geometric calibration model is applied to transform the image coordinates to geographic coordinates. The images calibrated by the geometric model are the Level1A (L1A) product. Due to the inaccuracy of satellite attitude and orbit parameters, the geometric calibration model also exhibits errors, resulting in geolocation errors in the BNU-1 L1A product. Then, a geometric correction method is applied as the second step to find the control points (CPs) extracted from the BNU-1 L1A product and the corresponding MODIS images. These CPs are used to estimate and correct geolocation errors. The BNU-1 L1A product corrected by the geometric correction method is processed to the Level1B (L1B) product. Although the geometric correction method based on CPs has been widely used to correct the geolocation errors of visible remote sensing images, it is difficult to extract enough CPs from polar images due to the high reflectance of snow and ice. In this study, the geometric correction employs an image division and an image enhancement method to extract more CPs from the BNU-1 L1A products. The results indicate that the number of CPs extracted by the division and image enhancements increases by about 30% to 182%. Twenty-eight images of Antarctica and fifteen images of Arctic regions were evaluated to assess the performance of the geometric correction. The average geolocation error was reduced from 10 km to ~300 m. In general, this study presents the geolocation method, which could serve as a reference for the geolocation of other visible remote sensing images for polar observations.

On-Orbit Geometric Calibration from the Relative Motion of Stars for Geostationary Cameras

Affected by the vibrations and thermal shocks during launch and the orbit penetration process, the geometric positioning model of the remote sensing cameras measured on the ground will generate a displacement, affecting the geometric accuracy of imagery and requiring recalibration. Conventional methods adopt the ground control points (GCPs) or stars as references for on-orbit geometric calibration. However, inescapable cloud coverage and discontented extraction algorithms make it extremely difficult to collect sufficient high-precision GCPs for modifying the misalignment of the camera, especially for geostationary satellites. Additionally, the number of the observed stars is very likely to be inadequate for calibrating the relative installations of the camera. In terms of the problems above, we propose a novel on-orbit geometric calibration method using the relative motion of stars for geostationary cameras. First, a geometric calibration model is constructed based on the optical system structure. Then, we analyze the relative motion transformation of the observed stars. The stellar trajectory and the auxiliary ephemeris are used to obtain the corresponding object vector for correcting the associated calibration parameters iteratively. Experimental results evaluated on the data of a geostationary experiment satellite demonstrate that the positioning errors corrected by this proposed method can be within ±2.35 pixels. This approach is able to effectively calibrate the camera and improve the positioning accuracy, which avoids the influence of cloud cover and overcomes the great dependence on the number of the observed stars.

System Calibration Including Time Delay Estimation for GNSS/INS-Assisted Pushbroom Scanners Onboard UAV Platforms

Unmanned aerial vehicles (UAVs ) equipped with imaging sensors and integrated global navigation satellite system/inertial navigation system (GNSS/INS ) units are used for numerous applications. Deriving reliable 3D coordinates from such UAVs is contingent on accurate geometric calibration, which encompasses the estimation of mounting parameters and synchronization errors. Through a rigorous impact analysis of such systematic errors, this article proposes a direct approach for spatial and temporal calibration (estimating system parameters through a bundle adjustment procedure) of a GNSS/INS -assisted pushbroom scanner onboard a UAV platform. The calibration results show that the horizontal and vertical accuracies are within the ground sampling distance of the sensor. Unlike for frame camera systems, this article also shows that the indirect approach is not a feasible solution for pushbroom scanners due to their limited ability for decoupling system parameters. This finding provides further support that the direct approach is recommended for spatial and temporal calibration of UAV pushbroom scanner systems.

Iterative feature detection of a coded checkerboard target for the geometric calibration of infrared cameras

The geometric calibration of cameras becomes necessary when images should be undistorted, geometric image information is needed or data from more than one camera have to be fused. This process is often done using a target with a checkerboard or circular pattern and a given geometry. In this work, a coded checkerboard target for thermal imaging cameras and the corresponding image processing algorithm for iterative feature detection are presented. It is shown that, due in particular to the resulting better feature detectability at image borders, lower uncertainties in the estimation of the distortion parameters are achieved.