Direct Georeferencing Accuracy Research Articles

Accuracy Assessment of UAV Photogrammetry System with RTK Measurements for Direct Georeferencing

Abstract. The direct georeferencing accuracy of unmanned aerial vehicle (UAV) images with real-time kinematic (RTK) measurements is a concerned topic in the community of photogrammetry. This study assesses the capabilities of a multi-rotor platform equipped with RTK technology, specifically a DJI Phantom 4 RTK UAV, for robust direct georeferencing. The UAV surveyed a square and a building at Wuhan University to assess the accuracy and spatial consistency of direct georeferencing in close-range photography. We tested checkpoint errors under various ground control points (GCPs) configurations. The results show that without GCP, an analysis of 71 spatially distributed checkpoints produced a root mean square error (RMSE) of 5.58 cm in the Z direction. This finding indicates that RTK-equipped UAVs can achieve acceptable error margins even without using GCPs, thereby fulfilling the precision requirements for large-scale mapping.

GEOMETRIC EVALUATION OF GAOFEN-7 STEREO DATA

Abstract. China’s first sub-metre stereo satellite, GaoFen-7, was launched on 7 November 2019. One of the main criteria for a stereo mapping satellite is the geometric accuracy of the images. In this paper, we present a systematic evaluation of the geometry accuracy of Gaofen-7 on two scenes over the centre of Munich, Germany. The geometry accuracy is evaluated in a three-step workflow: 1) direct georeferencing accuracy; 2) image orientation using bundle adjustment with ground control points; 3) height accuracy of the generated digital surface model (DSM). In addition to dense LiDAR point clouds, ground control points were measured in the field. These were used as references. The results show that RPC bundle adjustment with 0 order bias correction is sufficient to achieve sub-metre absolute accuracy. The height accuracy of the generated digital surface models varies with land cover type, ranging from 0.9m (NMAD) in open areas to 4.5m in urban areas.

EXPLOITING POLE-LIKE OBJECTS FROM CADASTRES FOR SUB-METRE ACCURATE INTEGRATED GEOREFERENCING OF LOW-COST MOBILE MAPPING SYSTEMS

Abstract. Accurately georeferenced data acquired using mobile mapping systems is of great importance for many geospatial applications. The accuracy of direct georeferencing – the standard procedure in the field of outdoor mobile mapping – strongly relies on GNSS reception and therefore varies greatly depending on the environment. By incorporating control point observations, integrated georeferencing enables homogenous accuracy and reliability over the whole mapping perimeter. Unfortunately, exact measurement and documentation of control points is needed, which often must be done manually. To automate this process, approaches from the field of autonomous vehicles use pole-like objects to support localization in complex urban environments, with the disadvantage of requiring a prior mapping campaign. However, various classes of pole-like objects have been recorded with accurate location and entered in public cadastres, so they could serve as 2D control points. In this paper, we present an approach for improving the trajectory accuracy in challenging urban environments by means of integrated georeferencing. It uses range image observations to pole-like objects from publicly available cadastres. Our approach achieves sub-metre accuracy, with a maximal cross-track difference of 98 cm, using real-world data acquired with our low-cost mobile mapping system. We further demonstrate that it significantly improves discontinuities and inaccuracy peaks in direct georeferencing and that the limiting factors are errors in the depth estimation of available range imaging sensors.

Geo3o1: a tool for 3D georeferencing accuracy assessment of tri-stereo images by state-of-the-art sensor dependent orientation model

We subjected a tool for 3D georeferencing accuracy assessment of tri-stereo primary images of Pléiades 1A/1B/Neo, SPOT 6/7, and Göktürk-1, developed in the MATLAB environment considering their private sensor-dependent orientation model. Although the orientation model has been developed by the image vendors, the major contribution of Geo3o1 was the two-step adjustment process. The basic stages of estimating the 3D georeferencing accuracy were (i) estimating the direct georeferencing accuracy by raw orientation parameters, (ii) pre-adjustment of look angles using ground control points (GCPs), and (iii) bundle adjustment. In addition, the efficiency and validation of exterior orientation parameters and also the correlation among interior and exterior orientation parameters could be analyzed. Geo3o1 has the capability of handling the images in stereo or tri-stereo modes. The case study has been carried out by the Pléiades 1A panchromatic tri-stereo primary images with 50-cm geometric resolution. About 90 points were used as GCPs/ICPs in two distribution scenarios. The accuracy on ground was estimated centimeter level at GCPs whereas the accuracy was naturally coarser at ICPs at ∼ ± 1 GSD. The efficiency and validation of exterior orientation parameters and the correlations were also shown by two samples. The research was carried out over an undulating, mountainous area in Zonguldak, Türkiye.

INITIAL STUDY ASSESSING THE SUITABILITY OF DRONES WITH LOW-COST GNSS AND IMU FOR MAPPING OVER FEATURELESS TERRAIN USING DIRECT GEOREFERENCING

Abstract. Drone technology has shown the potential to act as the middle ground between satellite, light aircraft, and terrestrial or in-situ methods. However, featureless terrain such as water poses a challenge when it comes to drone mapping. The main challenge is identifying matching points to combine overlapping images into a single dataset. In particular, because traditional methods such as Structure from Motion (SfM) is dependent on tie point collection, its usage over featureless terrain is almost impossible. In solving this problem, we propose that the use of Direct Georeferencing (DG) in registering images be explored as a potential method and we propose a method for correcting errors due to tilt with low-cost IMUs. This study first assesses the accuracy of direct georeferencing using low-cost Inertial Measurement Units (IMU) and Global Navigational Satellite System (GNSS) providing analysis of the error sources associated with direct georeferencing and then demonstrates new approaches to minimize them. To best simulate a water type environment or surface for the initial studies, a drone survey was conducted on flat farmland and a POSE analysis was performed. We then processed the images using direct georeferencing and then compared our error minimisation method to standard Bundle Block Adjustment with GCPs and again with no GCPs. Results showed that using the method proposed in this study helped reduce the Mean Absolute Error associated with direct georeferencing by 54%. These initial results show a clear potential for mapping over inland water using direct georeferencing.

REAL-TIME DRONE MAPPING BASED ON REFERENCE IMAGES FOR VEHICLE FACILITY MONITORING

Abstract. Facilities such as road, parking lots play an important role in our lives nowadays. Damage to such a vehicle facility can cause human injury, as well as inconvenience and cost. To prevent this, facility monitoring is performed periodically, but the current monitoring method is low efficiency by blocking the facility or performing it late at night. In order to increase the efficiency of monitoring, research using images, especially drone images, was conducted. However, when using a drone image, there is a trade-off relationship between accuracy and processing time. In this study, we propose a real-time drone mapping based on reference images for efficient vehicle facility monitoring. The real-time drone mapping based on the reference image is composed of reference images build, aerial triangulation (AT) based on reference images (refAT), and orthophoto generation. The refAT refers to a method of performing AT by using a reference images as reference data. We compared the processing time and processing accuracy of direct georeferencing and refAT. We built 154 drone reference images in the target area. The refAT showed a processing time of about 8.95 seconds and an accuracy of 3.4 cm, and the direct georeferencing method showed a processing time of about 1.49 seconds and an accuracy of 22.5 m. If the method of this study is used for facility monitoring, it is expected that the efficiency of monitoring will be improved with speed and accuracy.

Adaptive Re-weighted Block Adjustment for Multi-Coverage Satellite Stereo Images without Ground Control Points

Georeferencing satellite imagery is the basis for mapping and other subsequent processing in photogrammetry and remote sensing. Traditional georeferencing methods, which require adequate and well-distributed ground control points (GCPs), are time-consuming, costly, constantly cumbersome and inefficient. This study proposes a method of adaptive re-weighted block adjustment without GCPs for satellite stereo images. Furthermore, an approach of using multi-coverage images to improve the direct georeferencing accuracy is also investigated. The bias-compensated rational function model with affine transformation parameters is used as the adjustment mathematical model. The “weighted virtual control points” are introduced as constraints in the adjustment. Space intersection and resection are executed iteratively to solve the accurate affine transformation parameters. A total of 722 three-line array stereo scenes of Chinese Mapping Satellite-1 images captured from December 2010 to March 2016, which cover the entire Shandong Province of China (about 158,000 km2) nearly 10 times in space, are used as experimental data to evaluate the performance of the proposed method. Experiment results demonstrate that the proposed method can effectively eliminate the systematic errors of images. Moreover, the additional comparison with experiments of three groups of mono-coverage images indicates that the redundant observations from multi-coverage images can significantly improve the direct georeferencing accuracy, more specifically, from 13.69–16.02 m to 11.09 m in the horizontal direction, and from 16.39–29.27 m to 8.47 m in the vertical direction.

Rapid Disaster Mapping through Data Integration from UAVs and Multi-sensors Mounted on Investigation Platforms of NDMI, Korea

Abstract. As the recent disaster is so difficult to predict when and where it would hit, so it requires paradigm for disaster shifts from response to preparedness. In order to respond this change, NDMI has studied disaster scientific investigation (DSI) technologies for revealing systematically the root cause of disaster and protecting repetitive recurrence. The purpose of this study is to propose a convergence approach between data acquired from different types of sensors on a van-type investigation platform and UAVs of NDMI and assess their applicability for timely natural and man-made disaster mapping and monitoring. In order to evaluate its applicability for rapid disaster mapping, we pre-tested the proposed approach for NDMI site in Ulsan, Korea. For the enhancement of the direct geo-referencing accuracy of UAV imagery captured from on-board camera of DJI and the creation of more accurate map products, camera IOPs refinement and bundle adjustment were also performed with minimal GCPs. Finally, we conducted UAV data registration with LiDAR point clould for disaster mapping applications.

Improving the Accuracy of Direct Geo-referencing of Smartphone-Based Mobile Mapping Systems Using Relative Orientation and Scene Geometric Constraints.

This paper introduces a new method which facilitate the use of smartphones as a handheld low-cost mobile mapping system (MMS). Smartphones are becoming more sophisticated and smarter and are quickly closing the gap between computers and portable tablet devices. The current generation of smartphones are equipped with low-cost GPS receivers, high-resolution digital cameras, and micro-electro mechanical systems (MEMS)-based navigation sensors (e.g., accelerometers, gyroscopes, magnetic compasses, and barometers). These sensors are in fact the essential components for a MMS. However, smartphone navigation sensors suffer from the poor accuracy of global navigation satellite System (GNSS), accumulated drift, and high signal noise. These issues affect the accuracy of the initial Exterior Orientation Parameters (EOPs) that are inputted into the bundle adjustment algorithm, which then produces inaccurate 3D mapping solutions. This paper proposes new methodologies for increasing the accuracy of direct geo-referencing of smartphones using relative orientation and smartphone motion sensor measurements as well as integrating geometric scene constraints into free network bundle adjustment. The new methodologies incorporate fusing the relative orientations of the captured images and their corresponding motion sensor measurements to improve the initial EOPs. Then, the geometric features (e.g., horizontal and vertical linear lines) visible in each image are extracted and used as constraints in the bundle adjustment procedure which correct the relative position and orientation of the 3D mapping solution.

Extrapolated Georeferencing of High-Resolution Satellite Imagery Based on the Strip Constraint

Ground control points (<small>GCPs</small>) are necessary in order to achieve precise georeferencing of high-resolution satellite (<small>HRS</small>) imagery. However, measuring <small>GCPs</small> is costly, laborious, and time consuming. In some remote areas, we cannot even obtain well-defined <small>GCPs</small>. In this study, a strip constraint model is established. Based on the bias-compensated rational function model and the strip constraint model, a feasible extrapolated georeferencing approach for <small>HRS</small> imagery is presented. The presented approach remains effective even when the intermediate images in the strip are unavailable. Experimental results of the two ZiYuan-3 (<small>ZY-3</small>) nadir datasets show that the direct georeferencing accuracy of the <small>ZY-3</small> nadir images reaches only 9 to 12 pixels. With four <small>GCPs</small> in the first image, the georeferencing accuracy of the other images in the strip is improved to better than 2 pixels through extrapolated georeferencing.

Geometric Positioning for Satellite Imagery without Ground Control Points by Exploiting Repeated Observation

With the development of space technology and the performance of remote sensors, high-resolution satellites are continuously launched by countries around the world. Due to high efficiency, large coverage and not being limited by the spatial regulation, satellite imagery becomes one of the important means to acquire geospatial information. This paper explores geometric processing using satellite imagery without ground control points (GCPs). The outcome of spatial triangulation is introduced for geo-positioning as repeated observation. Results from combining block adjustment with non-oriented new images indicate the feasibility of geometric positioning with the repeated observation. GCPs are a must when high accuracy is demanded in conventional block adjustment; the accuracy of direct georeferencing with repeated observation without GCPs is superior to conventional forward intersection and even approximate to conventional block adjustment with GCPs. The conclusion is drawn that taking the existing oriented imagery as repeated observation enhances the effective utilization of previous spatial triangulation achievement, which makes the breakthrough for repeated observation to improve accuracy by increasing the base-height ratio and redundant observation. Georeferencing tests using data from multiple sensors and platforms with the repeated observation will be carried out in the follow-up research.

ORIENTATION MODELING FOR AMATEUR CAMERAS BY MATCHING IMAGE LINE FEATURES AND BUILDING VECTOR DATA

Abstract. With the popularity of geospatial applications, database updating is getting important due to the environmental changes over time. Imagery provides a lower cost and efficient way to update the database. Three dimensional objects can be measured by space intersection using conjugate image points and orientation parameters of cameras. However, precise orientation parameters of light amateur cameras are not always available due to their costliness and heaviness of precision GPS and IMU. To automatize data updating, the correspondence of object vector data and image may be built to improve the accuracy of direct georeferencing. This study contains four major parts, (1) back-projection of object vector data, (2) extraction of image feature lines, (3) object-image feature line matching, and (4) line-based orientation modeling. In order to construct the correspondence of features between an image and a building model, the building vector features were back-projected onto the image using the initial camera orientation from GPS and IMU. Image line features were extracted from the imagery. Afterwards, the matching procedure was done by assessing the similarity between the extracted image features and the back-projected ones. Then, the fourth part utilized line features in orientation modeling. The line-based orientation modeling was performed by the integration of line parametric equations into collinearity condition equations. The experiment data included images with 0.06 m resolution acquired by Canon EOS Mark 5D II camera on a Microdrones MD4-1000 UAV. Experimental results indicate that 2.1 pixel accuracy may be reached, which is equivalent to 0.12 m in the object space.

ORIENTATION MODELING FOR AMATEUR CAMERAS BY MATCHING IMAGE LINE FEATURES AND BUILDING VECTOR DATA

With the popularity of geospatial applications, database updating is getting important due to the environmental changes over time. Imagery provides a lower cost and efficient way to update the database. Three dimensional objects can be measured by space intersection using conjugate image points and orientation parameters of cameras. However, precise orientation parameters of light amateur cameras are not always available due to their costliness and heaviness of precision GPS and IMU. To automatize data updating, the correspondence of object vector data and image may be built to improve the accuracy of direct georeferencing. This study contains four major parts, (1) back-projection of object vector data, (2) extraction of image feature lines, (3) object-image feature line matching, and (4) line-based orientation modeling. In order to construct the correspondence of features between an image and a building model, the building vector features were back-projected onto the image using the initial camera orientation from GPS and IMU. Image line features were extracted from the imagery. Afterwards, the matching procedure was done by assessing the similarity between the extracted image features and the back-projected ones. Then, the fourth part utilized line features in orientation modeling. The line-based orientation modeling was performed by the integration of line parametric equations into collinearity condition equations. The experiment data included images with 0.06 m resolution acquired by Canon EOS Mark 5D II camera on a Microdrones MD4-1000 UAV. Experimental results indicate that 2.1 pixel accuracy may be reached, which is equivalent to 0.12 m in the object space.

GEOMETRIC CALIBRATION OF ZIYUAN-3 THREE-LINE CAMERAS COMBINING GROUND CONTROL POINTS AND LINES

Abstract. Due to the large biases between the laboratory-calibrated values of the orientation parameters and their in-orbit true values, the initial direct georeferencing accuracy of the Ziyuan-3 (ZY-3) three-line camera (TLC) images can only reach the kilometre level. In this paper, a point-based geometric calibration model of the ZY-3 TLCs is firstly established by using the collinearity constraint, and then a line-based geometric calibration model is established by using the coplanarity constraint. With the help of both the point-based and the line-based models, a feasible in-orbit geometric calibration approach for the ZY-3 TLCs combining ground control points (GCPs) and ground control lines (GCLs) is presented. Experimental results show that like GCPs, GCLs can also provide effective ground control information for the geometric calibration of the ZY-3 TLCs. The calibration accuracy of the look angles of charge-coupled device (CCD) detectors achieved by using the presented approach reached up to about 1.0''. After the geometric calibration, the direct georeferencing accuracy of the ZY-3 TLC images without ground controls was significantly improved from the kilometre level to better than 11 m in planimetry and 9 m in height. A more satisfactory georeferencing accuracy of better than 3.5 m in planimetry and 3.0 m in height was achieved after the block adjustment with four GCPs.

GEOMETRIC CALIBRATION OF ZIYUAN-3 THREE-LINE CAMERAS COMBINING GROUND CONTROL POINTS AND LINES

Due to the large biases between the laboratory-calibrated values of the orientation parameters and their in-orbit true values, the initial direct georeferencing accuracy of the Ziyuan-3 (ZY-3) three-line camera (TLC) images can only reach the kilometre level. In this paper, a point-based geometric calibration model of the ZY-3 TLCs is firstly established by using the collinearity constraint, and then a line-based geometric calibration model is established by using the coplanarity constraint. With the help of both the point-based and the line-based models, a feasible in-orbit geometric calibration approach for the ZY-3 TLCs combining ground control points (GCPs) and ground control lines (GCLs) is presented. Experimental results show that like GCPs, GCLs can also provide effective ground control information for the geometric calibration of the ZY-3 TLCs. The calibration accuracy of the look angles of charge-coupled device (CCD) detectors achieved by using the presented approach reached up to about 1.0''. After the geometric calibration, the direct georeferencing accuracy of the ZY-3 TLC images without ground controls was significantly improved from the kilometre level to better than 11 m in planimetry and 9 m in height. A more satisfactory georeferencing accuracy of better than 3.5 m in planimetry and 3.0 m in height was achieved after the block adjustment with four GCPs.

Cloud-Based Geospatial 3D Image Spaces—A Powerful Urban Model for the Smart City

In this paper, we introduce the concept and an implementation of geospatial 3D image spaces as new type of native urban models. 3D image spaces are based on collections of georeferenced RGB-D imagery. This imagery is typically acquired using multi-view stereo mobile mapping systems capturing dense sequences of street level imagery. Ideally, image depth information is derived using dense image matching. This delivers a very dense depth representation and ensures the spatial and temporal coherence of radiometric and depth data. This results in a high-definition WYSIWYG (“what you see is what you get”) urban model, which is intuitive to interpret and easy to interact with, and which provides powerful augmentation and 3D measuring capabilities. Furthermore, we present a scalable cloud-based framework for generating 3D image spaces of entire cities or states and a client architecture for their web-based exploitation. The model and the framework strongly support the smart city notion of efficiently connecting the urban environment and its processes with experts and citizens alike. In the paper we particularly investigate quality aspects of the urban model, namely the obtainable georeferencing accuracy and the quality of the depth map extraction. We show that our image-based georeferencing approach is capable of improving the original direct georeferencing accuracy by an order of magnitude and that the presented new multi-image matching approach is capable of providing high accuracies along with a significantly improved completeness of the depth maps.

Self-Calibration Adjustment of CBERS-02B Long-Strip Imagery

Due to hardware limitations, such as the poor accuracy of its onboard Global Positioning System receiver and star tracks, the direct georeferencing accuracy of the China and Brazil Earth Resource Satellite 02B (CBERS-02B) by its onboard position and attitude measurements is less than 1000 m at times. Thus, the image data cannot be directly used in surveying applications. This paper presents a self-calibration bundle adjustment strategy to improve the georeferencing accuracy of the onboard high-resolution camera (HRC). An adequate number of automatically matched ground control points (GCPs) are used to perform the bundle adjustment. Both the systematic error compensation model and the orientation image model along with the interior self-calibration parameters are used in the bundle adjustment to eliminate the systematic errors. A self-calibration strategy is used to compensate for the time delay and integrated charge-coupled device translation and rotation errors by introducing a total of ten interior orientation parameters. The preliminary results show that the accuracy of self-calibration bundle adjustment is two pixels better than that of bundle adjustment without self-calibration, and the planimetric accuracy of the check points is about 10 m. The unusual variations of the exterior orientation parameters in some cases are eliminated after enlarging the orientation image intervals and increasing the weights of the onboard position and attitude observations.

In‐orbit geometric calibration and validation of ZY‐3 three‐line cameras based on CCD‐detector look angles

AbstractA simple and feasible orientation method for the three‐line cameras of the ZiYuan‐3 (ZY‐3) satellite is introduced. The method calibrates the CCD‐detector look angles in the attitude determination reference (ADR) coordinate system rather than modifying the laboratory‐calibrated orientation parameters, which are not necessary for the geometric calibration. Additionally, the unknown parameters of the model are virtually uncorrelated. Experimental results show that the direct georeferencing accuracy of ZY‐3 nadir images have been improved from the kilometre level to better than 9 m in planimetry, and that of the forward‐and‐backward stereopair was also raised to better than 18 m in plan and 4 m in height. After a bundle adjustment using four ground control points, a higher accuracy of about 3·4 m in plan and 1·6 m in height was achieved.

On-Orbit Geometric Calibration of ZY-3 Three-Line Array Imagery With Multistrip Data Sets

ZY-3, which was launched on January 9, 2012, is the first stereo mapping satellite in China. The initial accuracy of direct georeferencing with the onboard three-line camera (TLC) imagery is low. Sensor geometric calibration with bundle block adjustment is used to improve the georeferencing accuracy. A new on-orbit sensor calibration method that can correct the misalignment angles between the spacecraft and the TLC and the misalignment of charge-coupled device is described. All of the calibration processes are performed using a multistrip data set. The control points are automatically matched from existing digital ortho map and digital elevation model. To fully evaluate the accuracy of different calibration methods, the calibrated parameters are used as input data to conduct georeferencing and bundle adjustment with a total of 19 strips of ZY-3 TLC data. A systematic error compensation model is introduced as the sensor model in bundle adjustment to compensate for the position and attitude errors. Numerous experiments demonstrate that the new calibration model can largely improve the external accuracy of direct georeferencing from the kilometer level to better than 20 m in both plane and height. A further bundle block adjustment with medium-accuracy ground control points (GCPs), using these calibrated parameters, can achieve external accuracy of about 4 m in plane and 3 m in height. Higher accuracy of about 1.3 m in plane and 1.7 m in height can be achieved by bundle adjustment using high-accuracy GCPs.

BUNDLE BLOCK ADJUSTMENT WITH SELF-CALIBRATION OF LONG ORBIT CBERS-02B IMAGERY

Abstract. CBERS-02B was the first high resolution earth observation satellite in China, which adopted linear array push-broom sensor. The nadir ground resolution of the on board HR camera was 2.36 m. However, the accuracies of the on-board GPS receiver and star tracker were very limited due to the technical restrictions. The accuracy of direct geo-referencing by the on-board measurements of position and attitude parameters was about 1 kilometre, which restrained the wide applications of the CBERS-02B imagery in the surveying and mapping field. It is necessary to perform the bundle block adjustment to improve the accuracy of geo-referencing. A proper sensor model has to be adopted during the bundle block adjustment using strict physical sensor model with long orbit data, in order to solve the problem of too many unknown exterior orientation parameters (EOPs). Several sensor models have been discussed, such as quadratic polynomial model, systematic error compensation model, orientation image model, and piecewise polynomial model. The combination of the systematic error compensation model and the orientation image model will be used to deal with the CBERS-02B imagery in this paper. Furthermore, three TDI-CCD linear arrays were fixed on the focal plane of the HR camera. The middle CCD array was shifted against the left and the right one. The level 1A image used in this paper was mosaicked by the three sub-images collected by the left, the middle and the right CCD, respectively. But there were some displacements among the three sub-images in the mosaicked image and the three CCD arrays may not be rigorously parallel. The angular parameter α and the translation parameters χ, γ of each CCD refer to the theoretical position on the focal plane is used to model the interior distortions, so there are totally 9 interior distortion parameters, although some of them are not significant. The laboratory calibrated parameters of the image sensor are usually different from the true values after launch. So a self-calibration strategy should be applied in the bundle block adjustment. Plenty of automatically matched GCPs with precision of 10 meters in plane and 20 meters in height are used to perform the bundle adjustment. Both the systematic error compensation model and the orientation image model with the interior selfcalibration parameters are used in the bundle block adjustment to eliminate the systematic errors caused by the camera internal distortions and to improve the precision of geo-referencing. A best combination of interior orientation parameters (IOPs) is drawn from the adjustment results with different combinations of these IOPs. Besides, there may be some gross errors in the automatically matched GCPs. The gross errors among GCPs may lead to unusual variation of the exterior orientation elements by time. Methods of enlarging the intervals of orientation image and increasing the weights of the position and attitude observations are applied in the combined bundle block adjustment to remove the influence of gross errors of GCPs. The preliminary experimental results show that for longer than 1000 km orbit data, the average accuracy of self-calibrated bundle block adjustment combined with GPS and star tracker observations is 2 pixels better than that without self-calibration. The planar position accuracies in X and Y of check points are 8 m and 7 m respectively.