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Independent methods for geometric calibration (GC) have become an important research direction in the field of optical satellite technology. The main purpose of this research is to eliminate dependence on ground calibration sites using relative constraints between images. Based on a systematic analysis of these relative constraints, we found that it was difficult, if not impossible, to completely eliminate ground constraints, although the number of ground control points (GCPs) required can be greatly reduced. To achieve practical GC with high accuracy and low cost, we proposed a new method to compensate for systematic errors in linear optical satellite data acquisition using only the relative constraints between two overlapped images, namely, the corresponding elevation constraints and sparse GCPs. We first demonstrated the feasibility of GC with relative constraints and established an optimized GC model suitable for these relative constraints. We then presented a global iterative method to eliminate inaccuracies in internal calibration caused by the different distributions of GCPs within two images. The nadir (NAD) linear camera on board the Zi-Yuan 3 (ZY-3) satellite was used to evaluate the feasibility of the presented GC method; the results indicated that the present method effectively compensated for systematic errors. Thus, this article demonstrated the feasibility of GC without calibration sites.

GaoFen1 is the first high-resolution earth observation satellite built in China, and carries four wide field-of-view (WFV) cameras to achieve large-scale monitoring and mapping. However, the unstable attitude measurement accuracy of the satellite generally imparts low geopositioning accuracy and inconsistent geometric error in overlapping areas of WFV images. A feasible and effective large-scale planar block adjustment (PBA) method is presented that corrects the geometric errors of the vast WFV images integrally, further improving the geometric accuracy of these images. In addition, whether ground control points (GCPs) are needed, and the effect of different numbers of GCPs on PBA accuracy is also investigated. Two key technologies are used in this paper. First, a universal PBA error equation based on the virtual control points is presented to allow PBA with or without GCPs. Second, an adjustment method aided by a digital elevation model is adopted to overcome the weak convergence geometry among WFV images, further ensuring stable estimation of PBA. The effectiveness of the proposed method was verified by 664 WFV images covering most of mainland China. The satisfactory experimental results indicate that the method presented herein is reasonable and effective, but that a certain number of GCPs is needed to ensure the accuracy of large-scale PBA results for WFV images.

Patch imaging is an important capability of GaoFen4, which is the first Chinese high-resolution planar array satellite in geosynchronous orbit. When the satellite collects images in the patch-imaging mode, overlapping images can be successively obtained. As these images are captured at different times and from a very high orbit, the initial geometric accuracy of the overlapping area between images is inconsistent, which directly affects image mosaicking. In this paper, a novel bundle block adjustment method based on the rational function model without ground control points (GCPs) is proposed to eliminate the relative geometric error among the images and to generate refined rational polynomial coefficients (RPCs). In this method, the average elevation is used instead of the true elevation of the ground points to solve the problem of weak convergence of corresponding bundles, which would result in unstable calculation in the height direction. Virtual control points (VCPs) generated from the original RPCs are used to restrain the freedom of the entire block in the horizontal direction, thereby ensuring stable calculation without the use of GCPs. A successive RPC regeneration method based on VCPs is also presented. To verify the effectiveness of the proposed method, four experiments were performed using real data to assess the geometric accuracy of the method, and the satisfactory experimental results indicate that the presented method is both practical and effective.

Due to the limitations in the geometric accuracy of reference data and matching accuracy between images from different sensors, the conventional method of calibration may not perform well in terms of accuracy for an agile optical satellite (AOS) with submeter resolution. Due to the high mobility of an AOS, a cross-image pair (CIP) including a push-broom image and a swing scanning image for the same area could be collected. This letter developed a novel calibration method for an AOS, which is characterized using the constraints from the CIP instead of reference images covering a calibration site. In this method, the viewing angle of a charge-coupled device detector fit by two polynomials is introduced into a rigorous imaging model to establish the calibration model, and a bundle adjustment under the constraint of an aided digital surface model (DSM) is applied to solve the unstable calculation stemming from the strong correlation between the parameters of the coupled images. To verify the effectiveness of the method, experiments were conducted using the CIP simulated according to the rigorous imaging process of an AOS. In tests, the method achieved a high theoretical accuracy of better than 0.1 pixels.