IEEE Geoscience and Remote Sensing Magazine - September 2023 - 34

be resolved in two major ways depending on whether the
original point clouds or resulting DEMs are available.
If the original lidar data strips are available, strip adjustment
offers a way to improve the geometric data quality
via minimizing the discrepancies found in the overlapping
region of the adjacent strips [89]. In general, the process
of strip adjustment begins via identification of common
shapes (such as rooftops or planar patches) extracted from
the point clouds or the derived DEM in the overlapping region.
Surface matching then determines and quantifies the
discrepancies between the two overlapping data strips.
A number of options are available for surface matching;
these include point-to-point analysis (e.g., the iterative closest
point), surface or linear feature matching [90], and so
on. Apart from using specific common shapes/objects for
surface matching, lidar intensity values can also serve as
another constraint for surface matching and subsequent
adjustment, and this is found predominantly in reducing
the horizontal discrepancy [91]. Finally, the strip discrepancy
between two strips can be minimized by applying a
3D geometric transformation of the strips (such as a 3D affine
transformation) or a correction to the previously mentioned
sensor parameters in (10) (i.e., a system calibration
approach). Interested readers are referred to [89].
If end users are unable to obtain the original lidar data
strips, there are also several solutions to resolve the stripe
artifacts in the resulting DEMs. Albani and Klinkenberg
[92] proposed a cross-smoothing algorithm with a probability
replacement function to constrain the elevation of
the striping in the DEM. Gallant [93] adopted a multiscale
adaptive smoothing filter to aid in DEM noise removal with
varying characteristics. Image filters based on wavelet and/
or Fourier transforms can help eliminate the stripe artifacts
in different orientations [94], [95]. All these provide
solutions to resolve the striping in lidar-derived DEMs,
which ultimately improves the DEM quality for various applications,
such as volcanic analyses [96].
SPECKLE/RANDOM NOISE
Similar to stripe artifacts, noisy returns may appear to be
scattered in the original point clouds as well as their data
derivatives, such as intensity images and CHMs. 3D point
cloud noise can be removed by postprocessing algorithms
based on spatial filtering, differences in elevations, or local
densities. Speckle intensity noise found on water surfaces,
although appearing to be difficult to handle, sheds light on
certain information for automatic water mapping.
SPECKLE INTENSITY NOISE ON WATER SURFACES
When an airborne lidar system surveys a water region, the resulting
intensity image may be corrupted by speckle noise in
addition to the effects of laser dropouts (i.e., data voids), as
previously mentioned (refer to the " Laser Dropouts on Water
Surfaces " section). Unlike passive optical remote sensing images
capturing a homogeneous reflectance over a water region,
the emitted laser beams undergo a series of scattering, reflection,
and refraction processes. When the incident angle is
almost perpendicular, specular reflection or even retroreflection
occurs, causing a strong reading of the echo amplitude
[97]. Since the instantaneous conditions of water bodies keep
changing, all these processes cause different levels of energy
transfer, leading to a variation of the backscattered laser pulse
strength [36]. In addition, if the gain control keeps changing
(as mentioned in the " Dark Objects/Weak Targets " section),
the collected intensity also varies even as the surveyed objects
remain the same [51]. All these effects result in the presence of
speckle noise in the intensity image (see Figure 16). The drawbacks
of speckle noise can be summarized in various ways.
(a)
(b)
(c)
(d)
FIGURE 16. Speckle intensity noise appears in different types of water regions, such as (a) inland rivers, (b) natural shores, (c) inland lakes,
and (d) rocky shores.
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IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE SEPTEMBER 2023

IEEE Geoscience and Remote Sensing Magazine - September 2023

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