Project Report

Spatial Fuser Pipeline Name

The LiDARMill Spatial Fuser project report is intended for data end users to review details of how mission data was acquired and processed, as both methodologies directly impact the accuracy and quality of the resulting data products generated.

Overview

Time

Coordinate Reference System

The project coordinate reference system is applied to all data imports and exports within the project. The selected project CRS is applied to imported survey control points, the post processed trajectory outputs, as well as the finalized LAS/LAZ and all corresponding data product outputs. Matching the project datum to the trajectory and survey control datum incurs the least amount of accuracy degradation.

Deliverable Products

This section presents a list of user selected output deliverable data products within Spatial Fuser Pipeline.

LiDAR

Contours

DTM

Digital Terrain Model derived from ground classified LiDAR returns

DSM

Digital Surface Model derived from highest hit LiDAR returns

CHM

Canopy Height Model derived from the difference in elevation between highest hit LiDAR returns and ground classified LiDAR returns

Decimated LiDAR DTM

Reduces the point density of LiDAR returns used to generate the Digital Terrain Model. The user specified value is used as the fixed/minimum point spacing between the output ground classified points

Acquisition

This section presents information pertaining to hardware used during data acquisition.

Hardware

Platform

Reference Station # (L1 Antenna Phase Center) - Geographic 3d CRS (User Specified Datum)

Data Processing

Trajectory Post Processing

The trajectory data is processed using Phoenix LiDAR System's Navlab. Navlab is used to refine the system's position and attitude. A highly accurate post processed trajectory is generated from the coupled integration of GNSS and IMU data collected by the LiDAR System during the scanning mission.

LiDAR Calibration

Trajectory Optimization

When the Trajectory Optimization tool is enabled, LiDARMill will perform feature matching using correspondences between overlapping swaths of LiDAR data to determine correction offsets that are applied to the mission’s trajectory. This will improve the resulting point cloud's relative accuracy.

Fluctuating trajectory accuracy statistics from NavLab are used in conjunction with offsets in overlapping swaths of LiDAR to determine necessary trajectory optimizations along flightlines, in order to further refine the alignment of LiDAR swaths.

Scanner Calibration

When the Scanner Calibration tool is enabled, the LiDARMill applies an angular correction to the LiDAR sensor (pitch, yaw, roll correction) to resolve misalignments from IMU to sensor. Depending on the LiDAR model, an additional ranging scale correction, tilt angle offset, or encoder calibration correction may be calculated and applied.

Systematic attitude (roll, pitch, heading) misalignments between the system's IMU and LiDAR sensor are computed and minimized by comparing overlapping swaths of Lidar.

Image Rectification

The Calibrate Camera tool will calibrate the intrinsic and extrinsic camera parameters. Camera calibration requires correctly setting initial focal length.

The initial image positions and orientations are adjusted using an automated tie-point matching algorithm within LiDARMill. After alignment, an automated balancing procedure is used to minimize radiometric differences caused by illumination changes.

Accuracy

There are two ways to differentiate high accuracy survey control points - Ground Control Points (CONTROL) and Survey Checkpoints (CHECK). CONTROL are surveyed points used for data adjustments, and CHECK are surveyed points used for accuracy reporting. CONTROL points utilized for data adjustments should never be used to validate the accuracy of the data product.

CONTROL and CHECK survey points are generally collected at the same time, using the same methodology. Survey CHECK points are points with known coordinates that are used to validate the accuracy of the survey. CONTROL points leverage GNSS data to adjust survey models and improve their overall accuracy. Unlike CONTROL, CHECK points do not affect how the LiDAR survey is processed in any way.

LiDAR Relative Accuracy

Relative accuracy, the measure of how well overlapping flightlines match each other, is determined for the mission(s). Surface models are developed for each flightline. Relative accuracy is calculated from these surfaces using two metrics, magnitude and dZ. Magnitude is the average of the absolute values of the vertical offsets between a single flightline surface and points from overlapping flightlines. dZ is the average value of the vertical offsets between a single flightline surface and the points from overlapping flightlines. An average magnitude for all flightlines represents the project's overall relative accuracy.

Vertical Adjustment (CONTROL)

LiDARMill determines vertical offsets between input CONTROL survey point elevations and ground classified point elevations within your point cloud. It then computes an average deviation magnitude, and vertically transforms the point cloud by the average magnitude, to best align the point cloud to the input CONTROL survey points. If there are no survey points labeled as "CONTROL", no vertical translation will be applied to trajectory/pointcloud.

A single vertical offset is determined and applied in order to optimize the fit of LiDAR with existing ground control.

LiDAR Absolute Accuracy (CHECK)

Independent survey checkpoints are utilized to assess absolute accuracy of the LiDAR pointcloud. The absolute accuracy of LiDAR data is determined from measured vertical offsets between surveyed checkpoints and the LiDAR point cloud. A TIN (Triangulated Irregular Network) surface is created from LiDAR ground classified points, and used to measure the vertical offsets to CHECK points.

Note: Although CONTROL points and corresponding Dz offsets are included in the table below, only CHECK points are utilized for absolute accuracy calculation.

The table below presents statistical information computed from the elevation differences found in the "Survey Control Point Report - LiDAR" Table. If there are no survey points labeled as "CHECK", neither a statistical summary nor Dz histogram plot is computed.

Survey Control Point Report - LiDAR

Table of elevation differences between the elevation of known survey CHECK points and the processed LiDAR pointcloud.

Dz Histogram

A plot displaying the histogram of the vertical distance error between surveyed CHECK point elevation values and the corresponding elevation value derived from the laser points at the survey CHECK point's XY location.

Maps

RGB

Pointcloud colored by corresponding RGB pixel values from mission imagery.

Intensity

Pointcloud colored by corresponding laser intensity.

Ellipsoidal Altitude

Pointcloud colored by corresponding ellipsoidal elevation values.

Height Above Ground

Pointcloud colored by distances above ground model derived from ground classified points

GCP Separation

Digital surface model with overlaid GCPs colored by dZ value.