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Large Scale Mapping: State-of-art technology in Aerial LIDAR and High Resolution Digital Camera imaging

LIDAR collection system uses a powerful laser sensor comprised of a transmitter and receiver, a geodetic-quality Global Positioning System (GPS) receiver and an Inertial Navigation System (INS) unit. The laser sensor is precision mounted to the underside of an aircraft. Once airborne, the sensor emits rapid pulses of infrared laser light, which are used to determine ranges to points on the terrain below. The time difference between the transmit pulse and the receive pulse is a measure of height.

Most LIDAR systems use a scanning mirror to generate a swath of light pulses. Swath width depends on the mirror’s angle of oscillation, and ground-point density depends on factors such as aircraft speed and mirror oscillation rate. Ranges are determined by computing the amount of time it takes light to leave an airplane, travel to the ground and return to the sensor. A sensing unit’s precise position and attitude, instantaneous mirror angle and the collected ranges are used to calculate 3-D positions of terrain points as many as 100,000 positions or “mass points” can be captured every second. This ability of LIDAR systems to capture accurate spot heights at an extremely rapid rate is the principle reason behind LIDAR's success.

However, a LIDAR system can discriminate among multiple returns from each Pulse, simultaneously surveying the canopy top and terrain. Multiple returns also can be used to determine intermediate surfaces such as treetops and power lines. In a treed area, for example, the first return may locate the top of a tree while the last return ideally locates the ground beneath the tree canopy. Multiple returns in between may represent branches, etc. All manmade and natural ground features are surveyed, including trees, buildings, cars, etc.

A color video camera records the area being scanned by the laser, and the video image is annotated with date and time. The top image features raw, unclassified LIDAR point data contours; the middle image is an example of traditional, computer-generated contours from stereo-compiled mass points and break lines; and the bottom image features

enhanced LIDAR contours with surface estimates using LIDAR points and break lines. After each day’s work, the data are downloaded and post processing commences. GPS data from the aircraft and multiple ground stations are processed together using sophisticated kinematic GPS post-processing software. The use of two or more ground stations provides quality control and improves the accuracy of the kinematic trajectory. As a result, the position (x, y, z) of an airborne GPS antenna at an interval of 0.5 or one second is calculated.

Subsequent to GPS processing, raw INS data and GPS trajectory are combined using advanced Kalman Filtering techniques. The outcome is a complete set of exterior orientation (EO) data (x, y, z, w, j, k) for sensor origin and output at a rate of 50Hz. Mass points (first and last return positions) then are computed using a combination of measured ranges, mirror scan angles and EO data. Various calibration parameters are input at this stage.

Accuracy Verification and Adjustment
Many applications, for example, contouring, require a bald-earth DTM. Unfortunately, the raw data points captured by LIDAR do not constitute a bald-earth DTM. Even though most LIDAR systems can measure "last return" data points, these "last-return" points often measure ground clutter like shrubbery, cars, buildings, and even the canopy of dense foliage. Consequently, raw LIDAR points must be post-processed to remove these undesirable returns. The degree to which this post processing is successful is critical in determining whether LIDAR is cost effective for large-scale mapping applications.

Processing of LIDAR data is quite complicated and need a number of corrections and artifact removals. There are different mathematical models to process the LIDAR data to refine the data and get accurate results. Filtering and Aerial triangulation techniques are part of LIDAR processing. Regression models and other data processing techniques are also being employed.

There are several commercial packages available for the post processing of LIDAR measurements. The (Optech, 2001) LIDAR system comes with a post-processing package. The parameter set for the algorithm and the artifacts observed in the processed data suggest that the algorithm is based on a morphological filter. (TerraSolid, 2001) offers a variety of LIDAR processing modules, including TerraScan for the filtering and thinning of LIDAR data. This package includes different methods for

slope-based filtering and thinning of LIDAR data. (INPHO GmbH, 2001) offers a product called SCOP for the derivation of DTM’s and contours from various sources, including LIDAR data. The approach for the LIDAR data processing is based on the method described in (Kraus, Pfeifer, 1998).

Sanborn has developed software packages e.g.; Filtering And Surface Estimation, (FASE); Process for Lidar Aerial Triangulation (PLAT) to improve the LIDAR data product accuracy.