IEEE Geoscience and Remote Sensing Magazine - September 2019 - 70

and context features) can decrease the adverse effects of
spectral confusion [66].

[57]. In temperate regions, natural vegetation presents
clear differences in seasonal changes for deciduous broadleaved plants (alternately leaf-on and leaf-off) and influences UIS identification accuracy. Selecting a suitable season can improve the effectiveness of UIS detection.
However, synthetic aperture radar (SAR) and lidar data
are less influenced by seasonal or external environmental
changes and have strong surface penetration, which allows
for all-weather, all-day-and-night, high-resolution Earth
observation imaging [58], [59]. Still, fluctuating ground
object changes remain a major challenge in lidar and SAR
imaging [60]-[62].
Additionally, different ground objects present varying
brightness due to different spectral reflection values. When
the brightness value of adjacent ground objects has a large
contrast, image segmentation will take the high brightness
value as the segmentation boundary, which can easily generate salt noise. It is also prone to pepper noise. The salt
and pepper noises manifest as white and black (light and
dark) spot noise, mainly generated by interference from
the imaging impulse sensor, transmission channel, decoding, and processing. The salt noise presents as high gray
noise, while the pepper noise shows up as low gray noise.
Moreover, salt and pepper noises can be produced in the
segmentation of RSIs [63]-[65].
Our goal is to alleviate the influence of spectral confusion in RSIs and improve the distinguishing of UIS from
other ground objects. Effective band math or band operational equations can improve the reflection value range of
UIS and suppress that of other ground objects to endow
forms with obvious brightness and contrast that help to
identify and extract UIS from RSIs. Moreover, a multifeature extraction strategy (combined with spectral, spatial,

ACTIVE REMOTE SENSING CHALLENGES
SAR and lidar are active approaches to ground object observation imaging. SAR uses the synthetic aperture principle
to achieve high-resolution slope distance imaging. However, SAR images suffer from radar depression angle changes,
which can lead to radar layover, shadows, and foreshortening in images due to height differences in ground objects
[67]. Figure 6 shows the influence of the radar depression
angle. The latter is defined as b, the terrain slope angle
is a, and the local incident angle is d. Their relationship
can be expressed as d = 90c - (b + a). In Figure 6, if the
local incident angle d 1 0c, then SAR images present radar
layover (cyan). If the local incident angle is 0c 1 d 1 90c,
then SAR images present radar foreshortening (deep yellow). If the local incident angle d 2 90c, shadows (black)
appear [68].
Figure 6(a) and (b) depicts the influence of the radar
depression angle and of building height differences, respectively, in an SAR image. In Figure 6(a), the influence
results vary with the radar depression angle: the larger the
angle, the more easily layover appears and the more readily
shadows manifest. The smoothness of ground objects also
influences radar echoes. Smooth ground objects, such as
airfield pavement, truncated roofs, paved roads, and still
water, show less radar echo. Contrarily, the radar depression angle has little effect on the echo of a relatively coarse
ground surface [69].
In lidar images, land cover fluctuations lead to a
weakening of the ground echo, and it is more difficult
to identify its peak value [70], [71]. Lidar images must

β Radar Depression Angle
Elevation
Angle

Layover
Foreshortening

Plus
Directions
α

Shadows

Building 1

Building 2

Layover

Viewing Direction

Foreshortening
(a)

Building 3

Incidence
Angle

Terrain Slopes
Angle

Shadows
(b)

FIGURE 6. The influential forms of different building heights during SAR imaging. (a) The influences of the radar depression angle. (b) An
SAR image with different influential forms caused by various building heights.

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

SEPTEMBER 2019

IEEE Geoscience and Remote Sensing Magazine - September 2019

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - September 2019