IEEE Geoscience and Remote Sensing Magazine - September 2023 - 35

In optical remote sensing, multispectral image bands
can aid in deriving biophysical indices not only for the
retrieval of vegetation parameters but also assessing water
conditions, e.g., the Normalized Difference Water Index
(NDWI). With the invention of dual- and tri-channel lidar
systems, green and NIR laser channels can conceptually
serve such a purpose. However, Yan et al. [36] have
proved that speckle intensity noise found on water surfaces
degrades the quality of the derived NDWI used for
delineating water bodies from land. Such an issue is particularly
serious when dealing with high waves, turbulence,
and white caps because laser pulse returns tend to
interact with the water surface via Bragg scattering [37].
Apart from the inappropriateness of deriving the water
index, direct use of lidar intensity in a machine learning
classifier to extract water regions may suffer from the issues
of between-class spectral confusion and within-class
spectral variation [43]. This may result in salt-and-pepper
noise found in the classification results. Therefore, proper
treatment of the intensity should be instituted prior to information
extraction.
A number of attempts are reported to mitigate speckle
intensity noise through image filtering; these include the
median filter [98] and mean filter [99]. However, these filters
may oversmoothen the entire dataset, in which most
regions are, in fact, free of speckle noise contamination.
Incekara et al. [100] proposed to implement a mean-shift
segmentation on the intensity image for image smoothening
since it maintains the edge of a water lake while eliminating
the noise. To the best knowledge of the author, a
comprehensive solution that completely removes the intensity
speckle noise on water surfaces is yet to be available.
Nevertheless, since speckle noise can already be used to distinguish
water regions from other topographic features (the
use of the SLIER by [36], as described in the " Laser Dropouts
on Water Surfaces " section), other potentially useful
information that aids downstream analyses could, in principle,
also be harnessed. Our deepened understanding of
the nature of the noise from these efforts will eventually
establish the foundations for a more comprehensive solution
of noise removal.
3D POINT CLOUD NOISE
End users of airborne lidar data may notice noisy returns
hovering on the 3D point clouds. The drawbacks of these
anomalies, if not properly resolved, may cause strong convexities
in the data, leading to spikes in DEMs or heterogeneous
intensity in the projected images. The causes of these
defects can be ascribed to system and environmentally induced
factors, such as random/systematic errors of range
detection and instantaneous atmospheric conditions, e.g.,
rain, snow, the solar background, and so on.
A time-of-flight (TOF) lidar system determines
the
range of an on-ground object by measuring the arrival time
of backscattered laser echoes if the amplitude of its leading
edge exceeds a specific detection threshold. The target
SEPTEMBER 2023 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
range can then be computed by considering the round-trip
travel time and the speed of light. As a result, the accuracy
of range detection highly depends on the backscattered
echo's amplitude. If a certain degree of high-frequency
noise, caused by fluctuations in air currents, for instance, is
superimposed on the backscattered laser echo, it may cause
an upward noisy artifact that preempts the actual signal.
The TOF mechanism thus records a return earlier than it
should and recognizes it as a data point that is closer to the
system than its intended position. Such a scenario is named
timing jitter [101] [see Figure 17(a)]
Depending on the target range, size, and reflectivity,
the backscattered laser echoes may have similar shapes but
with different amplitudes. As shown in Figure 17(b), although
the echoes' peaks are identical to each other in the
time domain, the echo with the higher peak amplitude has
an upward shift of the signal passing the detection threshold
earlier than the one with the lower peak amplitude.
Similar to the scenario of timing jitter, the TOF mechanism
may recognize a return earlier than it should, resulting in
a noisy point hovering on the point cloud. Such a scenario
is commonly named RWE [101]. The timing jitter problem
is indeed hard to solve since it is a random error. The RWE,
on the other hand, can be treated as a systematic error that
can be adjusted by considering the time offset between the
pulse rise time of the transmitted and recorded echoes.
Adjusting the threshold over time can also account for the
variation of target properties and atmospheric attributes.
Apart from the aforementioned system-induced noisy
returns, airborne lidar systems are sensitive to instantaneous
environmental and atmospheric conditions, resulting
in unwanted returns recorded in the point clouds. Fire
smoke and cumulus clouds appear as various clustered
points located on top of the terrain surface (see the examples
in Figure 18). Although end users can adjust the
point clouds by removing these obvious anomalies manually,
the corresponding occlusion still causes the problem
Floating
Point
Threshold
Emitted
Pulse
Backscattered
Pulse
t1 t2
(a)
(b)
FIGURE 17. (a) Timing jitter: a certain degree of high-frequency
noise superimposed on the backscattered laser echo causes an upward
noisy signal that advances the time, exceeding the threshold.
(b) RWE: the leading edge detection of a backscattered laser echo
influenced by the pulse amplitude and width that may result in an
earlier record (t1) of the backscattered laser pulse. Regardless of
(a) and (b), floating points may hover on the point cloud.
35
Time
Power
Power
IEEE Geoscience and Remote Sensing Magazine - September 2023

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