St. Petersburg Coastal and Marine Science Center
Airborne Lidar Processing System (ALPS) Software
Introduction
Airborne Lidar Processing System (ALPS) software is developed
in an open-source programming environment on a Linux platform (Figure
1). It has the ability to combine the laser return backscatter digitized
at 1-nanosecond intervals with aircraft positioning information. This
solution enables the exploration and processing of the
Experimental Advanced Airborne Research Lidar (EAARL) data in an interactive or batch mode.
ALPS also includes modules for the creation of bare-Earth,
canopy-top, and submerged topography Digital Elevation Models (DEMs).
The EAARL system uses an Earth-centered coordinate and reference system
that removes the necessity to reference submerged topography data
relative to water level or tide gages (Nayegandhi and others, 2006).
|
Figure 1. Screenshot from an interactive session in ALPS
showing Red-Green-Blue (RGB) (top left) images, EAARL geo-referenced
rasters (top center), last and first return data (top and bottom right),
waveform analysis (bottom center), and flight line map display (bottom
left). ( Enlargement)
|
Lidar Data Processing
|
Figure 2. Sample waveform returns from vegetation and submerged topography (Wright and Brock, 2002). ( Enlargement)
|
For waveform-resolving instruments such as EAARL, the range is
determined in post-processing. Processing algorithms have been developed
to extract the range to the first and last significant return. The
shape of the waveform is determined by a number of sensor parameters and
backscattering properties of targets.
Some important sensor parameters include the shape of the laser
pulse, the receiver impulse function, and parameters describing pulse
spreading (Wagner and others, 2007). Algorithms are adjusted to tasks to
account for waveform complexity (Wagner and others, 2004).
ALPS uses the following algorithms to differentiate between
returns: the zero crossing of the second derivative is used to detect
the first return and the trailing edge algorithm is used to detect the
range to the last return; i.e., the algorithm searches for the location
prior to the last return where direction changes along the trailing edge
(Figure 2). In submerged environments, effects of refraction and change
in speed of light as it enters the water column are accounted for in
the “submerged topography” algorithm. The exponential decay of the
return signal through the water column is also determined based on the
clarity of the water column. These corrections are performed by
examining sample waveforms from spatially distributed locations in the
survey area to define constants for exponential decay of the laser at
the water surface and within the water column. A selection of constants
is defined in ALPS for different water column and depth conditions:
ranging from deep and clear water column to shallow and turbid water
column. Data processed for submerged topography are referenced to an
ellipsoid datum, which is independent of the elevation of the water
surface (Nayegandhi and others, 2004).
Random Consensus Filter
|
Figure 3. The image on the right shows a
vertical slice of data from the image on the left. The image on the
right shows a concentration of points on the ground and several
outliers. The data outside of the red rectangle highlights the points
that would be removed (Nayegandhi and others, 2004). ( Enlargement)
|
ALPS applies semi-automated statistical filtering methods to
remove false bottom returns and other outliers from the EAARL lidar
data. Erroneous (i.e., outlier) points might include reflections from
objects such as birds, multiple atmospheric effects (e.g., dust,
moisture), or multiple reflections from bright targets.
Two filtering methods within ALPS are used to extract ground
(bare-Earth) elevations from a point cloud of processed last returns:
Random Consensus Filter (RCF) and Iterative Random Consensus Filter
(IRCF). The RCF is based on the Random Sample Consensus (RANSAC)
paradigm, which was originally published by Fischler and Bolles (1981).
The filter uses a grid of non-overlapping square cells of user-defined
size overlaid onto the original point cloud.
The user defines the grid cell size and vertical tolerance
based on the topographic complexity and point sampling density of the
data. The maximum allowable elevation range within a cell is established
by the vertical tolerance. An iterative process searches for the
maximum concentration of points within the vertical tolerance, and
removes those points outside of the tolerance (Figure 3).
The IRCF algorithm uses the RCF algorithm and a triangulated
irregular network (TIN) model iteratively to progressively densify the
output point cloud. The RCF paradigm is used to label the initial point
cloud that represents the ground. All labeled ground points are
triangulated using Delauney's Triangulation to create a TIN model. The
points rejected from the first RANSAC iteration are treated as potential
ground points. Each triangulated facet within the TIN model is defined
as a three-dimensional plane, the equation of which is determined from
the vertices of the triangulated facet. All potential ground points
above or below each facet are classified as ground if they fall within
the user-defined vertical range (also called the TIN vertical width)
from the 3-D plane. The TIN model, created in the subsequent iteration
with the new set of classified ground points, is further densified by
adding all potential ground points within the vertical width for each
triangulated facet. This process continues for a pre-defined number of
iterations, or until less than 2% of potential ground points are added
to the final population of ground points.
References
Fischler, M.A., and Bolles, R.C., 1981, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography: Communications of the Association for Computing Machinery, v. 24, p. 381-395.
Nayegandhi, A., Brock, J.C., Wright, C.W., and O'Connell, M.J., 2006, Evaluating a small footprint, waveform-resolving lidar over coastal vegetation communities: Photogrammetric Engineering & Remote Sensing, v. 72, no. 12, p. 1407-1417.
Wagner, W., Ullrich, A., Melzer T., Briese, C., and Kraus, K., 2004, From single-pulse to full-waveform airborne laser scanners: potential and practical challenges: International Archives of Photogrammetry and Remote Sensing, v. 35, Part B3, p. 201-206.
Wagner, W., Roncat, A., Melzer, T., and Ullrich, A., 2007, Waveform analysis techniques in airborne laser scanning: International Archives of Photogrammetry and Remote Sensing, v. 36, Part 3, p. 413-418.
Wright, C.W. and J.C. Brock, 2002, EAARL: A lidar for mapping shallow coral reefs and other coastal environments,
in Proceedings of the Seventh International Conference on Remote
Sensing for Marine and Coastal Environments, Miami, FL, 20-22 May 2002,
Veridian International Conferences, Unpaginated CD-ROM.
Nayegandhi, A., Brock, J.C., Wright C.W., Clayton, T.D., and Mosher, L.A., 2004, Processing and Filtering ‘bare-Earth’ Topographic Data Acquired by NASA’s Experimental Advanced Airborne Research Lidar (EAARL),
in Proceedings of the American Society for Photogrammetry and Remote
Sensing (ASPRS) Annual Conference, Denver, CO, 23-28 May 2004.
Unpaginated CD-ROM.