Shadow-Based Hierarchical Matching for the Automatic Registration of Airborne LiDAR Data and Space Imagery

Imagery Network Fine Registration by Reference Point Cloud Data Based on the Tie Points and Planes

Cameras and laser scanners are complementary tools for a 2D/3D information generation. Systematic and random errors cause the misalignment of the multi-sensor imagery and point cloud data. In this paper, a novel feature-based approach is proposed for imagery and point cloud fine registration. The tie points and its two neighbor pixels are matched in the overlap images, which are intersected in the object space to create the differential tie plane. A preprocessing is applied to the corresponding tie points and non-robust ones are removed. Initial coarse Exterior Orientation Parameters (EOPs), Interior Orientation Parameters (IOPs), and Additional Parameters (APs) are used to transform tie plane points to the object space. Then, the nearest points of the point cloud data to the transformed tie plane points are estimated. These estimated points are used to calculate Directional Vectors (DV) of the differential planes. As a constraint equation along with the collinearity equation, each object space tie point is forced to be located on the point cloud differential plane. Two different indoor and outdoor experimental data are used to assess the proposed approach. Achieved results show about 2.5 pixels errors on checkpoints. Such results demonstrated the robustness and practicality of the proposed approach.

Extraction of Urban Objects in Cloud Shadows on the basis of Fusion of Airborne LiDAR and Hyperspectral Data

Feature extraction in cloud shadows is a difficult problem in the field of optical remote sensing. The key to solving this problem is to improve the accuracy of classification algorithms by fusing multi-source remotely sensed data. Hyperspectral data have rich spectral information but highly suffer from cloud shadows, whereas light detection and ranging (LiDAR) data can be acquired from beneath clouds to provide accurate height information. In this study, fused airborne LiDAR and hyperspectral data were used to extract urban objects in cloud shadows using the following steps: (1) a series of LiDAR and hyperspectral metrics were extracted and selected; (2) cloud shadows were extracted; (3) the new proposed approach was used by combining a pixel-based support vector machine (SVM) and object-based classifiers to extract urban objects in cloud shadows; (4) a pixel-based SVM classifier was used for the classification of the whole study area with the selected metrics; (5) a decision-fusion strategy was employed to get the final results for the whole study area; (6) accuracy assessment was conducted. Compared with the SVM classification results, the decision-fusion results of the combined SVM and object-based classifiers show that the overall classification accuracy is improved by 5.00% (from 87.30% to 92.30%). The experimental results confirm that the proposed method is very effective for urban object extraction in cloud shadows and thus improve urban applications such as urban green land management, land use analysis, and impervious surface assessment.

Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations

Registration of large-scale optical images with airborne LiDAR data is the basis of the integration of photogrammetry and LiDAR. However, geometric misalignments still exist between some aerial optical images and airborne LiDAR point clouds. To eliminate such misalignments, we extended a method for registering close-range optical images with terrestrial LiDAR data to a variety of large-scale aerial optical images and airborne LiDAR data. The fundamental principle is to minimize the distances from the photogrammetric matching points to the terrestrial LiDAR data surface. Except for the satisfactory efficiency of about 79 s per 6732 × 8984 image, the experimental results also show that the unit weighted root mean square (RMS) of the image points is able to reach a sub-pixel level (0.45 to 0.62 pixel), and the actual horizontal and vertical accuracy can be greatly improved to a high level of 1/4⁻1/2 (0.17⁻0.27 m) and 1/8⁻1/4 (0.10⁻0.15 m) of the average LiDAR point distance respectively. Finally, the method is proved to be more accurate, feasible, efficient, and practical in variety of large-scale aerial optical image and LiDAR data.