Quality Assurance and Quality Control of Remote Sensing Systems

In the last few years, the mapping community has witnessed significant developments in using passive and remote sensing technologies onboard space-borne, airborne, and terrestrial platforms to provide a wide range of products. These developments can be ascribed to 1) proliferation of high resolution space borne imaging satellites operating in different portions of the electromagnetic spectrum, 2) reduced cost and improved performance of modern color, multispectral, and hyperspectral digital cameras, 3) continuous developments in Light Detection and Ranging (LiDAR) systems, 4) capability of integrated Global Navigation Satellite Systems/Inertial Navigation Systems (GNSS/INS) in providing accurate position and orientation information for the utilized platforms, 5) incorporation of multiple cameras and/or laser scanners onboard a single platform, 6) emergence of non-traditional mapping platforms such as airborne and terrestrial unmanned autonomous systems - UAS, 7) convergence of research efforts from the mapping and computer vision communities, and 8) increased demand for geospatial data to satisfy the needs of non-traditional applications (e.g., precision farming, infrastructure monitoring, powerline clearance evaluation, and construction engineering management).

Taking advantage of such developments in the remote sensing technologies is only possible when standard Quality Assurance and Quality Control (QA/QC) procedures are in place to ensure the utmost precision of the mapping product. In this chapter, the term “Quality Assurance — QA” is used to denote pre-mission activities focusing on ensuring that a process will provide the quality needed by the user. On the other hand, the term “Quality Control — QC” is used to denote post-mission procedures for evaluating the quality of the final product. QA mainly deals with creating management controls including the calibration, planning, implementation, and review of data collection activities.

For an illustration of standard QC activities, one can refer to the well-established photogrammetric procedures for evaluating the internal/relative and the external/absolute accuracy of the final product. For the evaluation of the internal/relative quality (IQC) of the outcome from a photogrammetric reconstruction exercise, we typically use the a-posteriori variance factor and the variance-covariance matrix resulting from the bundle adjustment procedure. As for the external/absolute quality (EQC) evaluation, checkpoint analysis using independently measured targets is usually performed. Since the computation of the LiDAR point cloud is not based on redundant measurements, which are manipulated in an adjustment procedure, standard photogrammetric IQC measures are not possible. Moreover, the irregular and sparse nature of the LiDAR point cloud makes the EQC process more challenging. A commonly used EQC procedure compares the LiDAR surface with independently collected control points. Besides being expensive, this procedure does not provide accurate verification of the horizontal quality of the LiDAR points, unless specifically designed targets are utilized. Such inability is a major drawback since the horizontal accuracy of the LiDAR points is known to be inferior to the accuracy of these points in the vertical direction. In this regard, this Chapter addresses the validation of remote sensing data from space borne, airborne, and terrestrial platforms.