We propose a distributed bundle adjustment (DBA) method using the exact Levenberg-Marquardt (LM) algorithm for super large-scale datasets. Most of the existing methods partition the global map to small ones and conduct bundle adjustment in the submaps. In order to fit the parallel framework, they use approximate solutions instead of the LM algorithm. However, those methods often give suboptimal results. Different from them, we utilize the exact LM algorithm to conduct global bundle adjustment where the formation of the reduced camera system (RCS) is actually parallelized and executed in a distributed way. To store the large RCS, we compress it with a block-based sparse matrix compression format (BSMC), which fully exploits its block feature. The BSMC format also enables the distributed storage and updating of the global RCS. The proposed method is extensively evaluated and compared with the state-of-the-art pipelines using both synthetic and real datasets. Preliminary results demonstrate the efficient memory usage and vast scalability of the proposed method compared with the baselines. For the first time, we conducted parallel bundle adjustment using LM algorithm on a real datasets with 1.18 million images and a synthetic dataset with 10 million images (about 500 times that of the state-of-the-art LM-based BA) on a distributed computing system.

In this letter, a new digital elevation model (DEM)-aided bundle block adjustment (BBA) method is proposed which utilizes a rational-polynomial-coefficient affine transformation model and a preconditioned conjugate gradient (PCG) algorithm with multisource satellite imagery (ZY-3 and GF-1) for producing and updating ortho maps of large areas. To deal with the weak geometry of the large blocks, a reference DEM is used in this method as an additional constraint in the BBA. The PCG algorithm is applied to solve the large normal matrix produced by the massive data of the large areas. Our proposed method was tested on three blocks of real data collected by GF-1 panchromatic and multispectral sensors and ZY-3 three-line-camera sensors. The preliminary results show that the proposed method can achieve an accuracy of better than 0.5 pixels in planimetry and is suitable for wide application in ortho-map production. It also has great potential for the ortho-map production of superlarge areas such as the country of China as one block.

Extensive applications of Zi-Yuan 3 (ZY-3) satellite imagery of China have commenced since its on-orbit test was finished. Most of the data are processed scene by scene with a few ground control points (GCPs) for each scene; this conventional method is mature and widely used all over the world. However, very little work has focused on its application to super large area blocks. This letter aims to study mapping applications without GCPs for a super large area, which is defined as a range of interprovincial or even nationwide areas. The automatic matching and bundle block adjustment (BBA) software developed by our research team are applied to deal with two blocks of ZY-3 three-line camera imagery which covers most of the provinces in eastern China. Our comparison analysis of different data processing methods and the geolocation accuracies of the overlapped areas between adjacent strips are presented in this letter, as well as the possibility of nationwide BBA. The preliminary test results show that multistrip BBA without GCPs can achieve accuracies of about 13-15 m in both planimetry and height, which means that nationwide BBA is considered practical and feasible.

Airborne light detecting and ranging (LiDAR) systems have been widely used for the fast acquisition of dense topographic data. Regrettably, coordinate errors always exist in LiDAR-acquired points. The errors are attributable to several sources, such as laser ranging errors, sensor mounting errors, and position and orientation system (POS) systematic errors, among others. LiDAR strip adjustment (LSA) is the solution to eliminating the errors, but most state-of-the-art LSA methods neglect the influence from POS systematic errors by assuming that the POS is precise enough. Unfortunately, many of the LiDAR systems used in China are equipped with a low-precision POS due to cost considerations. Subsequently, POS systematic errors should be also considered in the LSA. This paper presents an aerotriangulation-aided LSA (AT-aided LSA) method whose major task is eliminating position and angular errors of the laser scanner caused by boresight angular errors and POS systematic errors. The aerial images, which cover the same area with LiDAR strips, are aerotriangulated and serve as the reference data for LSA. Two types of conjugate features are adopted as control elements (i.e., the conjugate points matched between the LiDAR intensity images and the aerial images and the conjugate corner features matched between LiDAR point clouds and aerial images). Experiments using the AT-aided LSA method are conducted using a real data set, and a comparison with the three-dimensional similarity transformation (TDST) LSA method is also performed. Experimental results support the feasibility of the proposed AT-aided LSA method and its superiority over the TDST LSA method.

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.

This letter introduces a novel method for camera pose determination from monocular urban oblique images. Horizontal right angles that widely exist in urban scenes are used as geometric constraints in the camera pose determination, and the proposed 3-D measurement method using a monocular image is presented and then used to check the accuracy of the recovered image's exterior orientation parameters. Compared to the available vertical-line-based camera pose determination method, our new method is more accurate.

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.