The GaoFen-14 (GF-14) satellite is China’s most recent high-resolution earth observation satellite system. It is equipped with a two linear-array stereo camera and is intend for topographic mapping at 1:10,000-scale without ground control points (GCPs). The technical parameters of payloads will change once the satellite enters orbit. As a result, strict on-orbit geometric calibration is necessary. This study performs the on-orbit geometric calibration of the GF-14 stereo camera using ground calibration data. The exterior orientation errors are corrected by a comprehensive bias matrix, internal orientation errors are described by a fifth order polynomial. The results of Yinchuan calibration field show that the planar accuracy is better than 286 m (RMS) before calibration, and improved to 2.11 m (front camera) and 1.51 m (back camera) by external orientation, and further improved to 1.46 m (front camera) and 0.99 m (back camera) after internal calibration. The front intersection accuracy reaches 0.67 m in plane (RMS) and 1.10 m in elevation (RMS), respectively. Checking by multiple global check points (CKPs), the planar and elevation RMS reaches 2.38 m (5.09 m in CE90) and 2.08 m (3.43 m in LE90), respectively.
空中三角测量是实现相机参数在轨标定有效途径,将变化了的立体相机进行整体重组,提高无控定位精度.在整体参数解算时,由于影像的宽高比(影像宽度与轨道高的比值)太小,导致主距的改正数得不到正确值,影响相机参数在轨标定结果.文章在基于线阵-面阵混合配置的CCD影像光束法平差基础上,提出相机参数渐进标定的方法,并利用"天绘一号"03 星数据进行试验验证.试验结果表明,该方法能有效削弱宽高比太小对标定结果的影响,减小系统误差,提高卫星影像无控定位精度.
On-orbit geometric calibration is a key link for satellites to achieve high-precision positioning. In this paper, based on the 1∶2000 digital calibration test field in Ningxia, the calibration parameters of the dual-line-array cameras are calculated as a whole by the alternate iteration of forward intersection and backward intersection, and the high-precision on-orbit geometric calibration of the dual-line-array cameras of the GF-14 satellite is achieved. The calibration results were tested by using many testing fields around the world. The test results show that after high-precision geometric calibration, the accuracy of the direct forward intersection of the GF-14 satellite image can reach 2.34 m in plane and 1.97 m in elevation without ground control.
The US Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) satellite adopts 532 nm single-photon lidar with shallow sea bathymetry capability. In order to realize high-precision and automated shallow sea bathymetric mapping based on ICESat-2 photon data, an adaptive underwater point denoising algorithm that considers the search direction and search size is proposed in this article, and a detailed data processing process is discussed to further verify the technical feasibility. The accuracy of direct bathymetry and active–passive fusion bathymetry from ICESat-2 is systematically analyzed using airborne in situ data. First, the underwater photon points are separated by surface position identification; then, the signal point cloud search strategy is improved, the search size increases with water depth, the search angle is rotated and the direction of the maximum number of point clouds is taken as the main direction, and the threshold is automatically determined by histogram Gaussian fitting of the point cloud density to achieve automatic underwater signal extraction; then, the refraction correction is carried out based on the light geometry and the water depth is obtained; finally, the active–passive fusion bathymetry is performed by combining the optical remote sensing images WorldView-2 and Sentinel-2, and the accuracy is verified by using the airborne lidar bathymetric data provided by NOAA. The experimental results show that the proposed denoising algorithm can accurately discriminate the underwater signal/noise, and the overall accuracy is better than 86%; the root-mean-square error (RMSE) of ICESat-2 direct bathymetry is between 0.42 and 0.98 m; the RMSE of active–passive fusion bathymetry is between 0.84 and 1.88 m. Our workflow and experimental results demonstrate a means of using ICESat-2 to produce relatively accurate bathymetric maps in shallow, clear water environments.
GF-14 satellite is a new generation of sub-meter stereo surveying and mapping satellite in China, carrying dual-line array stereo mapping cameras to achieve 1:10000 scale topographic mapping without Ground Control Points (GCPs). In fact, space-based high-precision mapping without GCPs is a challenging task that depends on the close cooperation of several payloads and links, of which on-orbit geometric calibration is one of the most critical links. In this paper, the on-orbit geometric calibration of the dual-line array cameras of GF-14 satellite was performed using the control points collected in the high-precision digital calibration field, and the calibration parameters of the dual-line array cameras were solved as a whole by alternate iterations of forward and backward intersection. On this basis, the location accuracy of the stereo images using the calibration parameters was preliminarily evaluated by using several test fields around the world. The evaluation result shows that the direct forward intersection accuracy of GF-14 satellite images without GCPs after on-orbit geometric calibration reaches 2.34 meters (RMS) in plane and 1.97 meters (RMS) in elevation.
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GF-14 is one of the highest mapping accuracy satellites in China, and is adopted advanced multi-load integrated for earth observation technology, which is mainly used for high accuracy location and mapping 1∶10 000 geographic information products on a global wide. In this paper, the payload, ground processing flow and its performance were briefly introduced, then the geometric performance of the satellite images was evaluated using different fields including domestic and foreign areas. As a result, the location accuracy without ground control points (GCPs) of single strip can reach 1.8 m in horizontal and 0.80 m in vertical elevation in domestic areas and 1.76 m in horizontal and 0.82 m in vertical elevation in foreign areas, which can reach the best known level in international optical photogrammetry on location accuracy without GCPs.
针对光子计数激光雷达数据特点,研究基于泊松分布的点云去噪算法并开展精度评估.首先,将点云投影到二维剖面,划分格网并统计每个格网光子点个数,剔除点数大于平均值部分以计算背景噪声率;随后,从小到大调整格网尺寸,统计各尺寸下格网内的点数,大于阈值时将该网格内的点都标记为信号,并且根据比例大小划分为高、中、低置信度三类;最后,采用分段直线拟合将倾斜点投影到直线上以识别倾斜地形,采用分段二次拟合方法剔除残余孤立噪点,得到优化结果.利用多组不同地形光子点云数据开展实验,结果表明:基于泊松分布的去噪算法在冰盖、海洋场景下效果较好,整体精度优于96％,在植被场景稍差,但能达到识别信号的基本目标.
