Alternative methodologies for the quality control of LiDAR systems

Abstract

The ever improving capabilities of the direct geo-referencing technology (GNSS/INS) is having a positive impact on the widespread adoption of LIDAR systems for the acquisition of dense and accurate surface models over extended areas. Unlike photogrammetric techniques, derived footprints from a LIDAR system are not based on redundant measurements, which are manipulated in an adjustment procedure. Consequently, we do not have the associated measures (e.g., variance component of unit weight and variance-covariance matrices of the derived parameters), which can be used to evaluate the quality of the final product. In this regard, LIDAR systems are usually viewed as a black box that lacks a well defined set of quality control procedures. This paper introduces alternative procedures for evaluating the quality of LIDAR data. The main premise of the proposed methodologies is that overlapping LIDAR strips will represent the same surface if and only if there are no biases in the derived surfaces. Therefore, we will use the quality of coincidence of conjugate surface elements in overlapping strips as the basis for deriving the quality control measures. The paper starts with an analysis of error sources in a LIDAR system and its impact on the resulting surface. This analysis will be followed by several procedures to derive quantitative measures for evaluating the quality of the LIDAR data. These methodologies include the manipulation of range and intensity images generated from the irregular LIDAR data. Then, we will introduce methodologies, which are based on extracted linear features and areal patches from overlapping strips. The last approach is based on the manipulation of the irregular LIDAR points in a surface matching procedure to evaluate the quality of coincidence between conjugate surface elements in overlapping LIDAR strips. The paper will evaluate the performance of these procedures using real datasets. The paper will conclude by a comparative analysis that evaluates the pros and cons of the proposed methodologies in terms of complexity, validity, and the accuracy of the derived measures.