Lihua Tong

Extraction of Urban Power Lines from Vehicle-Borne LiDAR Data

Airborne LiDAR has been traditionally used for power line cruising. Nevertheless, data acquisition with airborne LiDAR is constrained by the complex environments in urban areas as well as the multiple parallel line structures on the same power line tower, which means it is not directly applicable to the extraction of urban power lines. Vehicle-borne LiDAR system has its advantages upon airborne LiDAR and this paper tries to utilize vehicle-borne LiDAR data for the extraction of urban power lines. First, power line points are extracted using a voxel-based hierarchical method in which geometric features of each voxel are calculated. Then, a bottom-up method for filtering the power lines belonging to each power line is proposed. The initial clustering and clustering recovery procedures are conducted iteratively to identify each power line. The final experiment demonstrates the high precision of this technique.

Semi-Automatic Registration of Airborne and Terrestrial Laser Scanning Data Using Building Corner Matching with Boundaries as Reliability Check

Data registration is a prerequisite for the integration of multi-platform laser scanning in various applications. A new approach is proposed for the semi-automatic registration of airborne and terrestrial laser scanning data with buildings without eaves. Firstly, an automatic calculation procedure for thresholds in density of projected points (DoPP) method is introduced to extract boundary segments from terrestrial laser scanning data. A new algorithm, using a self-extending procedure, is developed to recover the extracted boundary segments, which then intersect to form the corners of buildings. The building corners extracted from airborne and terrestrial laser scanning are reliably matched through an automatic iterative process in which boundaries from two datasets are compared for the reliability check. The experimental results illustrate that the proposed approach provides both high reliability and high geometric accuracy (average error of 0.44 m/0.15 m in horizontal/vertical direction for corresponding building corners) for the final registration of airborne laser scanning (ALS) and tripod mounted terrestrial laser scanning (TLS) data.

Multiscale Grid Method for Detection and Reconstruction of Building Roofs from Airborne LiDAR Data

This study proposes a multiscale grid method to detect and reconstruct building roofs from airborne LiDAR data. The method interpolates unorganized LiDAR point cloud into two sets of grids with different spatial scales. In the large-scale grid, building seed regions are obtained, including detection of initial building seed regions and refinement of building seed regions. In the small-scale grid, to detect the detailed features of building roofs with complicated top structures, a high-resolution depth image is generated by a new iterative morphological interpolation using gradually increasing scales, and then segmented by using a full λ-schedule algorithm. Based on the building seed regions, detailed roof features are detected for each building and 3-D building roof models are then reconstructed according to the elevation of these features. Experiments are analyzed from several aspects: the correctness and completeness, the elevation accuracy of building roof models, and the influence of elevation to 3-D roof reconstruction. The experimental results demonstrate promising correctness, completeness, and elevation accuracy, with a satisfactory 3-D building roof models. The strategy of hierarchical spatial scale (from large scale to small scale) obtains the complementary advantage between technical applicability in a large urban environment and high quality in 3-D reconstruction of building roofs with fine details.

Using unmanned aerial vehicle for remote sensing application

With the availability and development of multisensor, multitemporal, multiresolution, and multifrequency image data from operational Earth observation satellites, more limitations have also appeared in remote sensing image applications, and the most important one is the cost limit. To overcome the limitations, unmanned aerial vehicle (UAV) technology is developped for remote sensing application. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains data acquisition and processing technologies for imageries from UAV, as well as summarizes the current application research with UAV. Then, it presents some problems and prospective for UAV development. It is our hope that this survey will provide guidelines for future applications of UAV and possible areas.

Integration of LiDAR Data and Orthophoto for Automatic Extraction of Parking Lot Structure

To overcome the challenges of parking lot structure extraction using optical remote sensing images, this study proposes an automatic method for the extraction of parking lot structure by integrating LiDAR data and orthophoto, which consists of three steps. The first step is to extract vehicles from LiDAR data and then to identify the corresponding central axes for each vehicle. In the second step, orientations of the identified vehicle central axes are used as principle orientation constraints for parking lines extraction from orthophoto. The third step is the determination of parking lot structure with vehicle central axes and parking lines, in which parking lot parameters are calculated and an adaptive growth method is used for parking lot structure determination. In this method, vehicle central axes identified from LiDAR data and parking lines extracted from orthophoto are integrated for the extraction of parking lot structures. The main novelty of this study lies in two new algorithms: an algorithm on parking lines extraction with principal orientation constraints and an algorithm on parking lot structure determination based on parameter solution and adaptive growth. The experiment shows that the proposed method can effectively extract parking lot structure with high correctness, high completeness, and good geometric accuracy.

Hierarchical Registration Method for Airborne and Vehicle LiDAR Point Cloud

A new hierarchical method for the automatic registration of airborne and vehicle light detection and ranging (LiDAR) data is proposed, using three-dimensional (3D) road networks and 3D building contours. Firstly, 3D road networks are extracted from airborne LiDAR data and then registered with vehicle trajectory lines. During the registration of airborne road networks and vehicle trajectory lines, a network matching rate is introduced for the determination of reliable transformation matrix. Then, the RIMM (reversed iterative mathematic morphological) method and a height value accumulation method are employed to extract 3D building contours from airborne and vehicle LiDAR data, respectively. The Rodriguez matrix and collinearity equation are used for the determination of conjugate building contours. Based on this, a rule is defined to determine reliable conjugate contours, which are finally used for the fine registration of airborne and vehicle LiDAR data. The experiments show that the coarse registration method with 3D road networks can contribute to a reliable initial registration result, and the fine registration using 3D building contours obtains a final registration result with high reliability and geometric accuracy.

Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data

A new automated approach to the high-accuracy registration of airborne and terrestrial LiDAR data is proposed, which has three primary steps. Firstly, airborne and terrestrial LiDAR data are used to extract building corners, known as airborne corners and terrestrial corners, respectively. Secondly, an initial matching relationship between the terrestrial corners and airborne corners is automatically derived using a matching technique based on maximum matching corner pairs with minimum errors (MTMM). Finally, a set of leading points are generated from matched airborne corners, and a shiftable leading point method is proposed. The key feature of this approach is the implementation of the concept of shiftable leading points in the final step. Since the geometric accuracy of terrestrial LiDAR data is much better than that of airborne LiDAR data, leading points corresponding to anomalous airborne corners could be modified for the improvement of the geometric accuracy of registration. The experiment demonstrates that the proposed approach can advance the geometric accuracy of two-platform LiDAR data registration effectively.

Automatic Registration of Coastal Remotely Sensed Imagery by Affine Invariant Feature Matching with Shoreline Constraint

A new approach based on Affine Invariant Feature Matching (AIFM) with a filtering technique is proposed for automatic registration of remotely sensed image in coastal areas. The novelty of this approach is an automatic filtering technique using RANdom SAmple Consensus (RANSAC) with shoreline constraint for AIFM to remove all wrong matches and simultaneously keep as many correct matches as possible. To implement it, a progressive threshold strategy (from small value to large value) is presented to determine an appropriate RANSAC threshold, in which the progressive process is guided by shoreline constraint. The proposed approach (with filtering) is compared with standard AIFM (without filtering) using two typical image pairs in coastal areas. The experimental results indicate that the proposed approach can always provide much better matching results than standard AIFM.

Invariant triangle-based stationary oil platform detection from multitemporal synthetic aperture radar data

Abstract An automatic algorithm for stationary oil platform detection from multitemporal synthetic aperture radar data is proposed. The proposed algorithm consists of the following two parts. (1) A two-parameter constant false-alarm rate (CFAR) algorithm is used to extract targets from the Environment Satellite (ENVISAT) advanced synthetic aperture radar (ASAR), in which the focus is to determine the appropriate parameters of CFAR, thus ensuring as few as possible false-alarm targets when sea-surface targets are effectively extracted. (2) A simple point cluster matching pattern is proposed based on an invariant triangle rule, by which targets extracted from multitemporal ENVISAT ASAR images are automatically matched for detection of stationary targets (e.g., oil platforms). This invariant triangle rule is that any three moving targets have an extremely low probability of maintaining a relative position in multitemporal images, whereas stationary targets can always maintain a fixed relative position. Even under high noise, this invariant triangle rule can be used to realize the target data matching with high robustness. The experiment shows that the false-alarm rate and the missing rate are relatively low when all the targets are detected. The proposed invariant-triangle-based point cluster matching pattern can conduct effective detection and monitoring of stationary oil platforms.

Fusion of laser scanning data and optical high-resolution imagery for accurate building boundary derivation

Abstract A novel approach by the fusion of airborne laser scanning data and optical high-resolution imagery is proposed for automatically obtaining building boundaries with precise geometric position and details. The high-resolution images are used to directly extract the building boundaries with precise geometric position, and the laser scanning data are integrated to improve the correctness and completeness of the extracted boundaries. In this approach, a new method is first proposed to estimate the principal orientations of a building, based on the building image and rough principal direction constraints, which ensures the accuracy and robustness of the subsequent line segment extraction. On this basis, accurate boundary segments are extracted using a method based on laser scanning point density analysis and K-means clustering. Images from different sensors, including orthoimage, aerial stereo, or some other images, are able to be processed effectively. Experiments covering more than 200 buildings with various orientations, various structures, and various texture conditions are employed to test the proposed approach. The average correctness and completeness of the determined boundaries are 95% and 90%, respectively.