Airborne LiDAR Strip Error Correction and Deformation Monitoring

Airborne light detection and ranging (LiDAR) is a fast ground observation technology that can obtain point cloud data with centimeter precision in height and centimeter resolution in spatial domain, which has been widely applied to construct digital element model (DEM). However, there are still limitations in deformation monitoring, especially in large-scale and high-precision surface deformation monitoring. Namely, when the deformation magnitude is close to the LiDAR monitoring accuracy, the difference results contain obvious strip errors. To solve this problem, a strip error correction method based on the differential results is proposed for airborne LiDAR deformation monitoring including three main steps: quasi-stable adjustment on elevation correction, fixed-axis iterative closest point (ICP), and affine ICP algorithm to correct the strip errors. Experiments show that the proposed method can correct the strip error, and the RMSE of the two-phase point clouds for one flight track was decreased from the original 6.2 and 5.3 cm to 3.5 and 3.3 cm. Deformation patterns and rates are consistent as verified by external results with the small baseline subset InSAR (SBAS-InSAR) of Sentinel-1 SAR data. An internal comparison with the classical ICP algorithm shows this method is more accurate. The effectiveness of the proposed method in monitoring surface deformation is verified.

I. Introduction

Strip error in airborne light detection and ranging (LiDAR) refers to the systematic errors that affect the coordinate offsets for each strip separately. Summarizing current, the data-driven strip error correction methods can be divided into two categories, one is line and surface matching based on overlapping region features, such as the minimum Hausdorff distance (MHD) method [1] and conjugate linear features to adjust the strip error [2]. The other category is the iterative closest point (ICP) algorithm for aligning strips, such as the ICP algorithm for rigorous strip adjustment [3] and the coarse-to-fine strip mosaics model [4]. These methods only consider the best match for the overlapping portion of the strips, which can lead to a shift in the mosaic results. It does not affect the integrity of single-phase data, and the shift is usually ignored. However, in the field of deformation monitoring where the stability and reliability of the relative positions between multiphase point cloud data need to be ensured, this shift can have a large impact on the difference results, hence the limitations of these, the strip methods. In addition, error of single-phase data is not intuitive and cannot be corrected for its characteristics. Based on the above problems, this letter proposes a strip error correction method based on differential results. Most researchers use differential LiDAR data to obtain deformation results on the condition that the magnitude of deformation is usually much larger than the LiDAR accuracy [5]. However, when the deformation magnitude is close to the monitoring accuracy of LiDAR, we found that the centimeter-level strip error is visualized in the differential results. For example, Hu et al. [6] obtained the ground subsidence in the city of Locar, Spain, by two-phase LiDAR differencing. The results contained many strip errors, which also seriously affected the analysis of the ground subsidence. Therefore, we design three key steps to attenuate the striping error by analyzing the error characteristics and considering the systematic errors between the altitude reference and the LiDAR acquisitions of different orbits. The validity of the new method is illustrated by internal comparison of different algorithms and external validation of InSAR results.