IEEE Geoscience and Remote Sensing Magazine - September 2023 - 29

directions so as to minimize the effects of occlusions and
swath gaps. Very often, end users remove the data points
with large incident angles ()°152
since these off-nadir
data points suffer from significant laser energy attenuation,
resulting in stripe artifacts in the intensity image. If these
data points are incorporated in subsequent analyses, radiometric
correction should be implemented. More details can
be found in the " Intensity Striping at Swath Edges " section.
Detection of data voids within airborne lidar point
clouds can be made based on certain assumptions or
threshold techniques. Zhou and Vosselman [15] identified
the gaps of curb lines caused by the occlusions of on-road
vehicles, and only those gaps with a predefined length
(based on trial and error) were bridged. Yan [44] used the
time stamp and distance between two consecutive points
along the scan line as a criterion to look for laser dropouts
on water surfaces. Feng et al. [18] fitted a minimum bounding
rectangle to estimate whether a rooftop suffers from
partial occlusion caused by adjacent trees. Elberink and
Vosselman [49] adopted the rooftop area, orthogonal distance,
and shortest distance with corresponding predefined
thresholds to assess whether there exists a lack of data
points or gaps. Apart from making use of the raw lidar data
properties, various computational geometry and computer
vision methods are proposed via detecting and filling the
holes in triangular meshes [54], [55].
After gap/void detection, the design of a corresponding
filling mechanism highly depends on the type of voids/
gaps being found. A flattened water surface with a specific
elevation value can be assigned after compensating the laser
dropouts in the water region [44]. Studying the saliency
features, such as normal and curvature, can determine the
corresponding way to close the gaps found on rooftops
with a sparsity of data points [17]. On the other hand, a
sigmoid function along a spline can be used to reconstruct
the distorted curb lines caused by occlusion [15]. Therefore,
the filling and inpainting processes are object and
shape dependent.
Most of the time, data voids within
an airborne lidar dataset can be
detected and located. Nevertheless,
it is hard to decide the corresponding
filling mechanism, especially if the
dataset contains various voids found
in water regions, terrains, and objects
with low reflectance as well as at
steep slopes [56]. As a result, a future
direction should focus on a machine
learning approach to determine and
classify the types of voids being
found. Classification of data voids/
gaps is critical since it may affect the
inference of change detection. Data
gaps caused by building occlusions
should be considered since doing so
may infer whether there is a change
on the ground or not [57]. Despite a recent study on terrestrial
lidar that classified occlusions and laser dropouts on
terrains [58], research in point cloud void classification remains
in an early stage. A specific filling mechanism should
be designed for gradient changes of water slopes, such as at
waterfalls or downstream locations [59]. For those artificial
objects, point cloud completion or generative adversarial
networks (GANs) can be introduced to repair the missing
regions if intensive training data are available [60], [61],
[62], [63]. Persistent homology, which discerns topological
features from the point cloud through algebraic theories,
can also be introduced to determine significant holes from
the topological noise [64]. All in all, a comprehensive workflow
for automatic void detection, classification, filling/inpainting,
and reconstruction is desired.
STRIPE ARTIFACTS
Stripe artifacts are also referred to as rippling, periodic banding,
stairstepping, or corduroy in the literature. Such a defect
may not be spotted out until one interpolates the airborne
lidar point clouds to an intensity image or a DEM. An individual
lidar data strip may suffer from intensity banding
due to the misalignment between the field of view (FOV) of
the receiver and emitter. When combing multiple overlapping
lidar data strips, intensity striping may appear at swath
edges. This can be explained by the significant attenuation
of laser beams with longer ranges and larger incident angles.
Geometric misalignment between two overlapping swaths
may also lead to a rippling effect in the resulting DEM.
INTENSITY BANDING
If an airborne lidar data strip has a notable systematic striping
pattern found in the interpolated intensity image, the
lidar data highly likely suffer from intensity banding issues
(see Figure 10). Intensity banding occurs only if the lidar data
strip is collected by an airborne lidar system operated with
an oscillating mirror that has an incorrect optical alignment
[65], [66]. Therefore, the intensity banding effects caused by
(a)
(b)
FIGURE 10. (a) Stripe artifacts found in an intensity image generated by an individual lidar
data strip. (b) The intensity image after the removal of the banding effect.
SEPTEMBER 2023 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
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IEEE Geoscience and Remote Sensing Magazine - September 2023

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - September 2023