IEEE Geoscience and Remote Sensing Magazine - September 2023 - 22

wealth of topographic information represented by a set
of georeferenced 3D point clouds together with backscattered
laser signal strengths. Although such an active
remote sensing approach overcomes the limitations of
traditional imaging techniques [4], the unstructured and
bulky 3D point clouds are challenging for end users to
handle. The deficiency of airborne lidar data processing
software on the market can also be ascribed to the end users'
primitive understanding of the physical mechanism
behind how a lidar system works. Most end users simply
conduct visual examination of the collected 3D point
clouds through generating intensity images and DEMs/
digital surface models (DSMs). As a result, users may be
unaware or dismissive of the potential causes of data artifacts.
These underlying issues, if unmanaged, could affect
the overall reliability of any subsequent analysis based on
the compromised data.
System manufacturers and service providers make every
effort to collect and produce the highest quality of airborne
lidar data. While the lidar community puts strong emphasis
on the quality assurance (QA) and quality check (QC) of
data [5], [6], most of the QA and QC efforts are in modeling
system biases, which, in turn, rectifies only geometric accuracy.
Collected lidar data have unavoidable artifacts in the
form of speckle and/or random noise, anomalies, defects,
systematic biases, spikes, pitfalls, stairsteppings, periodic
stripings, corduroys, and so on, especially if users examine
the data at a sufficiently fine scale. Improvements in pulse
repetition frequency (PRF) and a finer laser pulsewidth [7]
further drive a significant increase of point cloud density
[8] and ultimately magnify these artifacts. These defects
not only result in unpleasant visual effects but also interfere
with the subsequent analyses. Commercial remote sensing
and photogrammetric software tools usually bundle with
data preprocessing modules for radiometric/geometric
correction, noise removal, image filtering, and spatial enhancement.
However, software tools for the airborne lidar
community are comparatively more rudimentary, with the
exception of a few prototypes created by researchers, e.g.,
LAStools [9], OPALS [10], and lidR [11]. Most commercial
software tools for lidar lack preprocessing functionalities
handling data artifacts. The inability to resolve these artifacts
at the beginning stage may render the extracted information
unreliable.
This review summarizes the artifacts commonly found
in airborne lidar data and their derivatives, the internal and
external factors underlying these defects, and the current
status of research handling and resolving these issues. It
should be noted, however, that this review centers on artifacts
that have significant visual and analytical impacts,
rather than on defects or noise reported in backscattered
lidar waveform signals [12]. In addition, although the term
" airborne lidar system " in this article mainly refers to topographic
airborne lidar, it also covers ALB, unmanned aircraft
system (UAS), and airborne photon-counting lidar.
Specifically, the focus of this concise review is on three
types of data artifacts (see Table 1 and Figure 1):
1) Data voids and holes appear as blank regions that frequently
occur in most topographic airborne lidar point
clouds. Data voids appear because of occlusions (i.e.,
shadows caused by elevated objects nearby), laser dropouts
found on water surfaces, ground objects with an
extremely low reflectance (i.e., dark objects), swath gaps
between data strips, or ground filtering. The presence of
data voids may cause unpleasant triangular meshes in
the resulting DEMs or intensity images, which, in turn,
affects the visual quality, topographic analysis, semantic
labeling, and 3D model reconstruction.
2) Stripe artifacts are commonly found in the resulting
intensity image. If stripes appear on an individual lidar
data strip, this is mainly attributed to misalignment
between the axes of the laser transmitter and receiver,
where this scenario is characterized as intensity banding.
Due to signal attenuation at swath edges, i.e., long
range and a large scan angle, intensity striping may appear
when combining two overlapping lidar data strips.
Similarly, striping may also appear in the overlapping
region of the resulting DEM if the respective lidar data
strips are not properly georeferenced and/or calibrated.
3) Random/speckle noise appears both in intensity images
and DEMs. Noisy random data points can be caused by
backscattered returns from unwanted floating objects
TABLE 1. AIRBORNE LIDAR DATA ARTIFACTS AND THEIR EFFECTS ON THE DERIVATIVES.
LIDAR DATA
DATA VOIDS AND HOLES
Point clouds
* Occlusions/shadows
* Laser dropouts
* Swath gaps
* Objects with low reflectance
* Dark objects/weak targets
* Ground filtering
Intensity image
DEM/DSM
* Huge triangular facets
* Missing intensity metrics
* Inaccurate understory information
* Triangular facets
* Discontinuous slope
* Inaccurate estimation of normal
STRIPES
* Imperfect instrument calibration
and alignment
* Banding (i.e., axial displacement
between emitter and receiver)
* Signal attenuation at large incident
angles and long ranges
* Periodic striping in individual strips
* Striping found at swath edges in
overlap region
* Periodic striping in the
overlapping region
SPECKLE/RANDOM NOISE
* Floating objects (e.g., clouds and birds)
* Bragg scattering and specular reflection
(e.g., water surfaces)
* Solar background noise (e.g., photon
counting)
* Range walk error/timing jitter
* Heterogeneous intensity on
homogeneous surfaces
* Intensity pits
* Craters/divots
* Blunders/spikes
* Pits/spikes in canopy height models
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IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE SEPTEMBER 2023
IEEE Geoscience and Remote Sensing Magazine - September 2023

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