Yanmin Jin

Integration of UAV-Based Photogrammetry and Terrestrial Laser Scanning for the Three-Dimensional Mapping and Monitoring of Open-Pit Mine Areas

This paper presents a practical framework for the integration of unmanned aerial vehicle (UAV) based photogrammetry and terrestrial laser scanning (TLS) with application to open-pit mine areas, which includes UAV image and TLS point cloud acquisition, image and cloud point processing and integration, object-oriented classification and three-dimensional (3D) mapping and monitoring of open-pit mine areas. The proposed framework was tested in three open-pit mine areas in southwestern China. (1) With respect to extracting the conjugate points of the stereo pair of UAV images and those points between TLS point clouds and UAV images, some feature points were first extracted by the scale-invariant feature transform (SIFT) operator and the outliers were identified and therefore eliminated by the RANdom SAmple Consensus (RANSAC) approach; (2) With respect to improving the accuracy of geo-positioning based on UAV imagery, the ground control points (GCPs) surveyed from global positioning systems (GPS) and the feature points extracted from TLS were integrated in the bundle adjustment, and three scenarios were designed and compared; (3) With respect to monitoring and mapping the mine areas for land reclamation, an object-based image analysis approach was used for the classification of the accuracy improved UAV ortho-image. The experimental results show that by introduction of TLS derived point clouds as GCPs, the accuracy of geo-positioning based on UAV imagery can be improved. At the same time, the accuracy of geo-positioning based on GCPs form the TLS derived point clouds is close to that based on GCPs from the GPS survey. The results also show that the TLS derived point clouds can be used as GCPs in areas such as in mountainous or high-risk environments where it is difficult to conduct a GPS survey. The proposed framework achieved a decimeter-level accuracy for the generated digital surface model (DSM) and digital orthophoto map (DOM), and an overall accuracy of 90.67% for classification of the land covers in the open-pit mine.

An Improved Weighted Total Least Squares Method with Applications in Linear Fitting and Coordinate Transformation

This paper presents an improved weighted total least squares (IWTLS) method for the errors-in-variables (EIV) model with applications in linear fitting and coordinate transformation. In addition, an improved constrained weighted TLS (ICWTLS) method is further obtained based on the IWTLS algorithm. Following the weighted TLS solution (WTLSS) method in which the precisions of any two columns of the design matrix differ only by a scalar factor in linear orthogonal regression problems, the IWTLS method is derived for a more generic case in which there is no proportionality assumption for the cofactor matrix of the design matrix in the EIV model. Compared with existing research on the constrained TLS method under the assumption that both the constraining matrix and the right-hand-side (RHS) vector are error-free, or that only the RHS vector contains errors, the ICWTLS method is proposed for resolving the EIV model with constraints by integrating the observation equations and constraint equations under the assu...

A Linear Feature-Based Approach for the Registration of Unmanned Aerial Vehicle Remotely-Sensed Images and Airborne LiDAR Data

Compared with traditional manned airborne photogrammetry, unmanned aerial vehicle remote sensing (UAVRS) has the advantages of lower cost and higher flexibility in data acquisition. It has, therefore, found various applications in fields such as three-dimensional (3D) mapping, emergency management, and so on. However, due to the instability of the UAVRS platforms and the low accuracy of the onboard exterior orientation (EO) observations, the use of direct georeferencing image data leads to large location errors. Light detection and ranging (LiDAR) data, which is highly accurate 3D information, is treated as a complementary data source to the optical images. This paper presents a semi-automatic approach for the registration of UAVRS images and airborne LiDAR data based on linear control features. The presented approach consists of three main components, as follows. (1) Buildings are first separated from the point cloud by the integrated use of height and size filtering and RANdom SAmple Consensus (RANSAC) plane fitting, and the 3D line segments of the building ridges and boundaries are semi-automatically extracted through plane intersection and boundary regularization with manual selections; (2) the 3D line segments are projected to the image space using the initial EO parameters to obtain the approximate locations, and all the corresponding 2D line segments are semi-automatically extracted from the UAVRS images. Meanwhile, the tie points of the UAVRS images are generated using a Forstner operator and least-squares image matching; and (3) by use of the equations derived from the coplanarity constraints of the linear control features and the colinear constraints of the tie points, block bundle adjustment is carried out to update the EO parameters of the UAVRS images in the coordinate framework of the LiDAR data, achieving the co-registration of the two datasets. Experiments were performed to demonstrate the validity and effectiveness of the presented method, and a comparison with the traditional registration method based on LiDAR intensity images showed that the presented method is more accurate, and a sub-pixel accuracy level can be achieved.

Bias-Corrected Weighted Total Least-Squares Adjustment of Condition Equations

AbstractThe total least-squares (TLS) method and its variations have recently received increasing research attention. However, little attention has been given to the weighted TLS adjustment method with condition equations. In this paper, a weighted TLS method designed for condition equations (WTLSC) is presented with the assumption that both the observation vector and design matrix contain errors. The covariance matrices are estimated for both the observation vector and design matrix after the adjustment, and the biases are corrected for the adjusted observation vector, design matrix, and corresponding covariance matrices in the WTLSC method. The proposed approach was used in an adjustment problem of an object point photographed by three terrestrial cameras. The results show that the proposed method resolves the condition equations with errors in the design matrix without linearization in the case study. The proposed WTLSC method generates stable error vector and matrix for the observation vector and desi...

Area-preservation Simplification of Polygonal Boundaries by the Use of the Structured Total Least Squares Method with Constraints

This article presents an area-preservation approach for polygonal boundary simplification by the use of structured total least squares adjustment with constraints (STLSC), with the aim being to maintain the area of the original polygons after the simplification. Traditionally, a simplified line is represented by criti- cal points selected from the original one. However, this study focuses on maintaining the areas of the polygons in the process of simplification of polygonal boundaries. Therefore, the proposed method in this article is a supplement to the existing line simplification methods, and it improves the quality of the sim- plification of polygonal boundaries in terms of positional and area errors. Based on the sub-divisions of the original polyline, using the critical points detected from the polyline by the use of line simplification methods, the framework of the proposed method includes three main components, as follows: (1) estab- lishment of the straight-line-segment fitting model based on both the critical and intermediate points on the sub-polyline; (2) introduction of both area and end-point constraints to reduce the geometric distor- tions due to the line simplification; and (3) derivation of the solution of boundary simplification by the use of STLSC. An empirical example was conducted to test the applicability of the proposed method. The results showed that: (1) by imposing the linear fitting model on both the critical and intermediate points on the sub-polylines in the proposed STLSC method, the positional differences between the original points and the simplified line are approximately in a normal distribution; and (2) by introducing both end-point and area constraints in the proposed STLSC method, the areas of the simplified polygons are the same as those of the original ones at different scales, and the two neighboring fitted lines are connected to each other at the optimized position.

A Comparison of the Performance of Bias-Corrected RSMs and RFMs for the Geo-Positioning of High-Resolution Satellite Stereo Imagery

High-resolution stereo satellite imagery is widely used in environmental monitoring, topographic mapping, and urban three-dimensional (3D) reconstruction. However, a critical issue in these applications using high-resolution stereo satellite imagery is to improve the accuracy of point geo-positioning. This paper presents a framework for comparison of the performance of the three-dimensional (3D) geo-positioning of the bias-corrected Rigorous Sensor Models (RSMs) and rational function models (RFMs) with respect to the high-resolution QuickBird stereo images in three spaces (i.e., orbital space, image space and object space). The compared models include a bias-corrected RSM in the orbital space, a bias-corrected RSM and RFM in the image space, and a bias-corrected RSM and RFM in the object space. In the comparison, the RSMs and RFMs use the vendor-provided orbit data and Rational Polynomial Coefficients (RPCs), respectively. The experimental results indicated that, (1) these five bias-corrected models can provide a sub-pixel geo-positioning accuracy. With the zero-order polynomial correction model in the orbital space and a minimum of three Ground Control Points (GCPs), the accuracy based on RPCs better than 0.8 m in horizontal direction and 1.3 m in vertical direction. With an increase in the number of GCPs, or in the order of correction models, the regenerated orbital parameters achieve a slight improved positioning accuracy of 0.5 m in horizontal direction and 0.8 m in vertical direction with 25 GCPs, which indicates that the low-order correction model in the orbital space can accurately model the effects of ephemeris and attitude errors; (2) the performances of bias-corrected RSM and RFM in image space are rather similar. However, the bias-corrected RSM and RFM in image space achieve a better accuracy than the bias-corrected RSM and RFM in object space, with the same configuration of GCPs.

Bundle adjustment of High Resolution Stereo Camera on Mars Express

The High Resolution Stereo Camera (HRSC) can obtain stereo imageries almost simultaneously along the track and avoid the changes of the Martian surface over time. According to the exposure time of image line number, the exterior orientation (EO) parameters of all conjugate points are extracted from the Spacecraft, Planet, Instrument, C-matrix, Events (SPICE). The interior orientation parameters are obtained from the camera calibration files. Due to existence of error in the exterior orientation parameters, bundle adjustment (BA) is carried out to remove the geometric inconsistencies. After BA, the object points calculated with the adjusted EO parameters are back projected onto the image plane and compared with the original observation. The experimental results show that sub-pixel accuracy is obtained after BA.

Attitude Jitter Detection Based on Remotely Sensed Images and Dense Ground Controls: A Case Study for Chinese ZY-3 Satellite

Satellite platform vibration induced by the onboard dynamic components and exterior perturbation deteriorates platform stability and causes attitude jitter, resulting in image distortion and geometric accuracy degradation. This paper presents an attitude jitter detection method based on images and dense ground controls, which requires neither high-performance attitude measurement devices nor specific sensor configuration like parallax observation. Attitude variations will result in image space discrepancies at control points, from which the attitude jitter can be retrieved. The method was validated by a case study with ZY-3 satellite, which is the first civilian high-resolution stereo mapping satellite in China. The experimental results show that an almost constant jitter frequency of about 0.65 Hz was detected, which was consistent with the direct attitude observations. The photogrammetric method is theoretically capable of detecting attitude jitter up to half of the image line scanning rate according to Shannons sampling theorem, which is far beyond the detectable range by direct attitude observations at a frequency usually not higher than 4 Hz. Currently, the attitude jitter of the roll angle and the pitch angle can be effectively detected and accurately estimated for the nadir camera. The pitch angle jitter is also well estimable for the forward and the backward cameras. With approximation, the roll angle jitter for the two off-nadir cameras can also be estimated, though with some deviation caused by the influence of the yaw angle jitter.

Total least L1- and L2-norm estimations of a coordinate transformation model with a structured parameter matrix

Total least L1- and L2-norm estimations of a symmetrical coordinate transformation model with a structured parameter matrix are proposed, with the aim to account for the relationships between the transformation parameters. In the model, the errors in the coordinates of the measured points in both the source and target coordinate systems in the transformation model are taken into account. The solution of the proposed symmetrical coordinate transformation model is derived by the use of the total least L1- and L2-norm estimations. In addition, the variance-covariance matrices of the estimated parameters and the adjusted coordinates of the points are further derived in the two proposed methods. A numerical experiment in coordinate transformation is conducted to test the proposed methods. The results show that the proposed total least L2-norm estimation method is suitable for resolving the transformation model when the coordinates of the points in both the source and target systems are contaminated only by random errors. However, in the case of gross errors in the coordinates of the points, the proposed total least L1-norm estimation method performs better than the total least L2-norm estimation, resulting in higher precision of the estimated parameters.

Uncertainty of Spatial Information and Spatial Analysis

Uncertainty of spatial information and spatial analysis is one of the most essential and fundamental issues in geographical information science, and spatial data quality plays a critical role in geographical applications and decision-making. This chapter adopts a bibliometric quantitative analysis to study the history of international development in this field, to study the state and evolution of hot issues, and to analyse the position of China in this field as well as the contribution of NSFC with respect to gross, process and source. The result shows that the research of Chinese scholars in this field is overall top-ranked worldwide. However, there exists a gap of high-influence achievement in this field between our country and international developed countries. Therefore, further efforts should be made to strength the fundamental researches on uncertainty and trust in spatial information and to associate with the strategic requirements of national development.