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Received：17 February 2022，
Published：07 March 2023
移动端阅览
正射校正是卫星遥感影像后续处理、分析与应用的必要基础，而目前光学卫星数据自主定位精度尚不能达到1—2像元，正射影像产品的生产仍需依赖地面控制点对成像几何模型进行修正。针对国产光学卫星数据的现状、特点和应用需求，中国遥感卫星地面站自主研发了国产光学卫星正射影像产品和自动正射系统，本文从精细化影像自动配准、成像几何模型优化、影像正射校正、几何不确定度评估、太阳照射和卫星观测角度计算、基于地理格网的影像剖分等方面对相关产品及其自动生成算法进行介绍。其中，基于L1范数约束最小二乘的RPC模型全参数优化方法，可在影像幅宽较大或几何定标精度不足时获得比常规附加像方仿射变换参数的RPC模型更高的校正精度；提出顾及控制点精度的区域网平差方法，在利用从空间分辨率较低的参考影像获取的控制点修正成像几何模型时，可在满足参考影像几何约束的前提下，通过多次观测减小成像几何模型的随机误差、提高影像的几何定位精度；基于不确定度传播理论，建立了正射影像产品逐像元几何不确定度评估方法。试验结果表明，本文算法可用于规模化的正射影像产品生产，分布于全国不同区域的实测检查点表明所生产的16 m分辨率国产高分正射影像产品绝对几何精度可达到2像元以内。
Orthorectification is the foundation for the subsequent processing
analysis
and application of satellite remote sensing images. However
the accuracy of the autonomous geo-positioning of the optical satellite data cannot reach 1—2 pixels at present. Ground Control Points (GCPs) are still required for correcting the geometric model to generate Digital Orthophoto Map (DOM) products. This study introduces the DOM products of Chinese optical satellites and automated processing algorithms independently developed by China Remote Sensing Satellite Ground Station. Given the current situation
characteristics
and application requirements of the Chinese optical satellite data
a complete and automated DOM generation algorithm has been developed. It consists of several key steps and techniques
including automated GCP collection via accurate image registration
optimization of image geometry model
image orthorectification
pixel-wise geometric uncertainty estimation
pixel-wise solar irradiation and satellite observation angle calculation
and image division and encoding based on a global or regional geographic grid. (1) Given that the conventional affine transformation correction in the image space for the Rational Polynomical Coefficients (RPC) model cannot achieve satisfactory accuracy when applied to perform a geometric model if the swath width of the image is large or the geometric calibration accuracy is insufficient
a full-parameter optimization method of RPC model based on L1-norm constrained least squares (L1LS) is applied to improve the accuracy of the geometric model. (2) A novel block adjustment method that considers the accuracy of GCPs is proposed. When the GCPs obtained from the reference image with the low spatial resolution are used to correct the geometric model
the random error of the geometric model can be reduced through multiple observations under the premise of meeting the geometric constraints of the reference image
thereby improving the geo-positioning accuracy of the image. (3) Based on uncertainty propagation theory
the per-pixel geometric uncertainty is derived from the geometric model
the Digital Elevation Model (DEM) data
and the residual errors of GCPs to enable the geometric accuracy traceability of DOM products. When applied to GF-1 MSS
GF-1 WFV
GF-6 PAN
HJ-2A CCD4
CB-4A MSS
and ZY-1E PAN images
the L1LS-based full-parameter optimization method of the RPC model outperforms the conventional affine transformation correction in the image space for the RPC model
particularly for the images with a large field of view. The proposed block adjustment approach considering the accuracy of GCPs achieves higher accuracy than the ordinary block adjustment method when GCPs are obtained from a reference image with a resolution (10—15 m) lower than that of the target image (2—2.5 m).Conclusion The experimental results show that the algorithm in this study can be used for the large-scale production of orthophoto products. The field-measured checkpoints distributed all over China show that the absolute geometric accuracy of the produced 16 m-resolution DOM products can achieve high accuracy within two pixels.
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