Airborne Laser Technology, Data Processing and Applications

Airborne Laser Technology, Data Processing and Applications
E. Baltsavias
Institute of Geodesy and Photogrammetry
ETH Zurich, CH-8093 Zurich, Switzerland
[email protected]
www.photogrammetry.ethz.ch
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Acknowledgements
For this presentation material has been used, without or with modifications, from various colleagues,
organisations and companies, who I want to thank:
-  C. Brenner (Leibniz University of Hannover)
-  N. Pfeifer and team (Technical University of Vienna)
-  D. Fritsch, J. Kilian, A. Wehr (Univ. of Stuttgart)
-  M. Doneus (Univ. of Vienna)
-  G. Vosselman, G. Sithole (ITC, TU Delft)
-  N. Demir (ETHZ)
-  A. Streilein (Swisstopo = Swiss Federal Office of Topography)
-  U. Lohr (at that time with firm Toposys)
-  P. Friess (Optech)
-  Y. Kuwano, A. Rohrbach (Leica)
-  A. Ullrich (Riegl)
-  Firms Leica, Riegl, Optech, IGI, Toposys, Fugro
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1
Contents
- History
- Basic components of Airborne Laser Scanning (ALS) and functioning
- Range measurement principles
- Interaction of laser beam with targets
- Full waveform digitising
- Basic error sources and data accuracy
-  Processing overview
-  Strip adjustment
-  Point classification (filtering)
- Overview of commercial systems (hardware and software)
- Overview of applications and examples
- Quality control of data
- Bathymetric lidar
- Short comparison of airborne laser scanning to other remote sensing technologies
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History
First optical laser developed in 1960 (Maiman, USA)
First airborne laser ranging tried out in 1960s
Started to be developed from early and middle 1970s, espec. in N. America, particularly
for hydrographic and bathymetric applications
First in late 1980s, the use of GPS made accurate range measurements from airborne laser
profilers possible (Univ. of Stuttgart, Prof. Ackerman)
Beginning of 90s profilers replaced by scanners (ALS), and GPS combined with IMU
1996 ISPRS Congress in Viennna: one ALS manufacturer, some reports and tests with ALS
1996-2000: work of several ISPRS Working Groups (WG) and one OEEPE WG on ALS.
Publication of a special issue of ISPRS Journal of Photo & RS gives a good overview.
Since 2000: ALS increasingly used in practice and in various applications; increasing
scientific investigations and tests and better methods; continuously improving ALS systems,
incl. waveform digitizing (espec. from 2004) and simultaneous double ALS (from 2006);
more and better software; more service providers and users
Often the term LiDAR is used (espec. In N. America): Light Detection And Ranging
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Basic components and functioning
-  Active sensor: a very narrow, high energy ray is sent from a source (laser) to the scene,
reflected back and recorded. Active means it works day and night (even better at night due
to no sun interference). Active also means it can measure in textureless areas including
shadows
-  Here we treat only airborne Lidar. What is recorded is the Time of Flight (TOF) or rarely
the phase (called also continuous wave (CW) lasers), and almost always the intensity,
although intensity is rarely used. All commercial ALS systems use TOF. There is one
experimental CW ALS, called SCALARS (Univ. of Stuttgart)
-  Basically: measurement of distance via polar technique, e.g. the direction of the ray and
the distance from the ray source to the scene are measured
-  Most ALS work in near infrared (NIR), so are influenced by clouds, snow, rain etc. (no
weather independence, as radar)
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Basic components
-  Laser transmitter and detector/receiver = range measuring unit
Receiver consists of: detector, electronics, optical filters, optics
-  Deflection mechanism of the laser ray, e.g. mirror, polygon
-  GNSS (not only GPS) / IMU (Intertial Measurement Unit ; sometimes term INS used)
Offsets (called also lever arm) between GNSS/IMU and laser, and misalignment between
IMU and laser (called boresight calibration) must be known.
GNSS is in differential modus with GNSS reference stations closeby. Dual frequency GNSS
is mostly used.
-  Computer, onboard software (e.g. for navigation and flight management) and storage
devices (data size is huge), including precise timing device that synchronises all sensors.
Sensors synchronised (e.g. using GNSS PPS (Pulse Per Second) time port) and their data
time stamped (e.g. with resolution ≤ 1 µsec)
-  Aircraft platform, mostly stabilised (important roll compensation, espec. for low altitude
flights). For helicopters possible use of pod.
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3
Basic components
Optionally cameras:
-  digital cameras (frame or less line CCDs) -> especially for generation of orthoimages
-  Video or standard CCDs for attributation or annotation (see powerline example in
applications)
-  less thermal, hyperspectral
Clear tendency: as of 2008 ca. 50% of ALS had additional imaging sensors.
Platforms
-  Airplanes
- Helicopters (espec. for mapping of corridors or small areas)
- Unpiloted Airborne Vehicles (UAVs), including small ones (developments from firm Riegl
for lightweight ALS, e.g. http://www.riegl.com/products/uasuav-scanning/)
- Much less, balloons and other nonconventional platforms.
Terrestrial (many) and satellite (very few) laser scanners not treated here.
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Basic components of an ALS system
DGNSS
Laser
transmitter
Control &
data recording
IMU
Deflection
unit
Detector/
Receiver
Ground
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4
Basic components of an ALS system
No fixed rules exist for distance of
ground reference GNSS stations
from airplane. Often 10-50 km,
depending on topography (GNSS
satellite visibility) and possible
GNSS signal disturbances.
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Pulse laser measurement principle
(pulse in reality not trapezoidal as shown here, but Gaussian)
•  Travelling time:
h=R
ttravel = 2R / c
with c speed of light
Ground
•  Example:
h = R = 1000 m  ttravel = 6.7 µs
•  Range resolution:
AT
AR
next pulse
t
ttravel
tp
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t
ΔR = ½ c Δttravel
•  Maximum pulse repetition frequency (theoretical,
assuming no transmit / receive overlap):
fmax = 1 / ttravel = c / 2R
•  Example:
h = R = 1000 m  fmax = 150 kHz
10
5
Pulse laser measurement principle
tp
Typical characteristics of sent pulse:
Signal
amplitude
•  Pulse width
tp = 10 ns ( 3 m @ speed of light)
t
trise
•  Pulse rise time
trise = 1 ns ( 30 cm @ speed of light)
•  In TOF, a position of the incoming pulse rising edge
is used to determine TOF and range. Intensity is
measured by the maximum or better the area of the
incoming pulse.
•  Peak power
Ppeak = 2,000 W
•  Energy per pulse
E = Ppeak · tp = 20 µJ
3m
•  Average power (@ pulse repetition rate F = 10 kHz)
Pav = E · F = 0.2 W
Average power constant for a system. Thus, energy
and Ppeak decrease with higher PRF F.
Ground
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CW laser measurement principle
Characteristics (example ScaLARS):
Modulation
signal
t
•  Two modulation frequencies
fhigh = 10 MHz, flow = 1 MHz
 λshort = 30 m, λlong = 300 m
Signal
amplitude
Modulation
signal
•  High frequency used for accurate phase measurement
t
•  Low frequency used for wavelength count (ambiguity for
number of wavelengths)
Left: measurement with
f high
AT, AR, transmitted and
received amplitude
Signal
amplitude
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T: period
tL : travelling time
(corresponds to phase
difference)
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6
CW laser operation
•  Maximum unambiguous range determined by λlong :
Rmax = λlong / 2
•  Example:
λlong = 300 m  Rmax = 150 m
Ground
•  Range resolution:
ΔR = λshort / 4π Δφ
AT
t
AR
t
λ
•  Range gating:
Range differences known to be < λlong / 2
•  Range tracking:
If no sudden surface steps > λlong / 2 are present
CW lasers will not treated further here
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Some definitions
-  Pulse repetition frequency (PRF) or pulse rate: number of pulses sent per second
-  Echoes (also called pulses or returns): received pulse reflections from multiple
objects recorded for one sent pulse
-  Minimum vertical object separation: minimum distance between 2 separable echoes
-  Scan rate: number of scan patterns (e.g. scan lines) per second
-  Field of View (FOV) or scan angle: across-flight angle that laser beam can cover
-  Beam divergence: the angle showing the deviation of the laser beam from parallelity
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7
Other important parameters
- Minimum and maximum flying height: maximum depends mainly on transmitted power, minimum on
national/local regulations and eyesafe distance
-  Maximum swath width: depends on flying height and FOV
-  Laser footprint (ground area illuminated by laser beam): depends on beam divergence and flying
height. In ideal case a circle, in reality an ellipse or even more irregular pattern
-  Wavelength: important for measuring certain objects (object should reflect well at laser wavelength)
-  Across and along track point density (these 2 define also the average point density): they depend on
many parameters, like scan pattern, PRF, scan rate, flying height, aircraft velocity, FOV etc. ->
necessity for good flight planning and selection of acquisition parameters
-  Number of echoes for which intensity is recorded
-  GNSS/IMU measurement frequency and accuracy (accuracy espec. for IMU)
-  Use of additional imaging sensors (digital cameras, video, etc.)
-  Weight, dimensions, power consumption, environmental operational conditions (Temp., Humid. etc.)
-  Range resolution and accuracy (note difference!)
-  Software! (flight planning, post-processing etc.)
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Scanning mechanisms & ground patterns
Oscillating
mirror
Rotating
polygon
Nutating mirror
(Palmer scan)
Fiber switch
(Toposys Falcon)
Laser
(same for
receiving
optics)
Z-shaped,
sinusoidal
Parallel
lines
“Elliptical”
Parallel
lines
Flight direction
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8
Scanning mechanisms & ground patterns
-  Most systems use oscillating mirrors with equal angle increments
-  Some systems offer selectable scan patterns (though differences not large)
-  Accuracy at the edge of the swath with oscillating mirrors often worse due to
deflection inaccuracies, espec. with high inertia mirrors
-  Point density is inhomogeneous with all scan patterns, for some patterns more for
other less (try to compensate this with appropriate flight planning parameters)
-  There are gaps in ground coverage and depending on laser footprint also
overlaps. This is one of the reasons why laser images (intensity) is much inferior
to digital camera images
-  With Palmer scan the point density at swath edge is higher. This is positive for
connecting neighbouring overlapping swaths (see strip adjustment below)
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Scanning mechanisms & ground patterns
Some systems offer selectable scan patterns (though differences not large), here
example Leica ALS
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9
Beam divergence
•  Laser beam widens with distance
•  Beam divergence γ
•  Theoretical limit by diffraction:
D, aperture diameter
γ ≥ 2.44 λ / D
•  Large receiving optics aperture generally advantageous
(collection of more reflected energy)
•  Example:
h
λ = 1064 nm, D = 10 cm  0.026 mrad
(γ / 2)
•  Typical values for ALS: γ = 0.15 – 1 mrad
•  Ground laser beam diameter (assuming a circle)
DI = D + 2h tan(γ / 2) ≈ 2h tan(γ / 2) ≈ h γ
DI, diameter of
illuminated area
•  Example:
γ = 1 mrad  1 m diameter @ h = 1 km flying height
•  Small divergence general advantageous:
- more homogeneous objects and terrain, less surface
smoothing
- better XY and Z accuracy
Ground
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Polygon mirror example
Parameters in green
…
…
0.08° = constant angular
step of mirror
…
Pre-decided
25 cm @
0.5 mrad
beam
divergence
θ/2=20°
h=500 m
0.08°  70 cm
Swath width
2h tan (θ / 2) = 0.7 h = 364m
…
…
…
∝ 1 / cos2 θi
364 m
0.08°  79 cm
83 cm
v = 150 km/h ; 50 lines / s
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Pulse repetition
frequency 25 kHz
500 pulses / line
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10
Basic components – The laser ray
Laser beam properties (for pulse lasers)
-  High power, so that enough energy can return back to the detector (high flying height).
-  Very narrow beam: laser can illuminate and measure small targets, more energy per area.
-  A very narrow high energy pulse is emitted, with a width of under 10 ns
(note 1 ns means 0.3 m distance). The narrower the pulse, the better the range accuracy.
- Pulse modelled as a Gaussian function, pulse width measured at half maximum of the
amplitude.
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Basic components – The laser ray
Spectral properties
-  Mostly used laser: Nd:YAG = neodymium-doped yttrium aluminium garnet
Emits at 1064 nm wavelength
-  Other systems: e.g. 810 nm (ScaLARS), 900 nm (FLI-MAP), 1540 nm (TopoSys, Riegl)
-  Laser systems emit in one wavelength only. Exception bathymetric lasers emit at
1064 and 532 nm, to measure both water surface and water bottom
-  Emitted light has very narrow spectral width, e.g. for Nd:YAG 0.1-0.5 nm
-  Experimental multispectral laser scanners developed for terrestrial applications (multiple
laser diodes, no range measurement). But no commercial ALS product exists.
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11
Spectral properties
Substantial differences
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Spectral properties (from JPL Aster spectral library)
Note the huge
reflectance difference
between
1.047-1.064 µm
(60%) (most ALS)
and
1.540 µm (0.6%)
(Toposys Falcon)
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12
Reflectivity
Reflectivity vs. Material (for 900nm laser wavelength)
MATERIAL
REFLECTIVITY
Dimension lumber (pine, clean,
dry)
94%
Snow
80-90%
White masonry
85%
Limestone, clay
up to 75%
Deciduous trees
typ. 60%
Coniferous trees
typ. 30%
Carbonate sand (dry)
57%
Carbonate sand (wet)
41%
Beach sands, bare areas in
desert
typically 50%
Rough wood pallet (clean)
25%
Concrete, smooth
24%
Asphalt with pebbles
17%
Lava
8%
Black rubber tire wall
2%
Range vs. reflectivity
Correction factor for maximum laser range,
depending on target reflectivity (example for lasers of
firm Riegl, 900 nm wavelength, diffuse targets,
maximum range in the specifications given for 80%
reflectivity). Range proportional to square root of
reflectivity.
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Types of reflection
Isotropic (Lambertian)
reflection
(same in all directions)
Specular reflection
(water, glas etc.)
 no received signal,
if angle α not very small
Local normal to surface
α
α
Local normal to surface
Mixed (hybrid) reflection
(partly isotropic, partly
specular)
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13
Power balance
Example
•  PT = 2000 W
Ar
R
1 - Power transmitted:
PT
•  Atmospheric transmission
M = 0.8
•  Receiver area Ar = 80 cm2 (for Dr =
10 cm)
2 - Power received on object:
M PT
•  Range = 1 km
3 - Power reflected, assuming
Lambertian reflection:
 Pr = 4 · 10-10 PT = 800 nW
PT M ρ / π
•  Reflectivity ρ = 0.5
•  H u g e d i f f e r e n c e b e t w e e n
transmitted and received power.
Main influence from R.
4 - Power received:
Ground
Pr = ρ PT (M2Dr2 Dtar2) / (4R4γ2) = ρ PT (M2Ar2) / (πR2)
Note influence of R2 (extended target); R3 , R4 (for linear , point target)
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For most ALS systems, pulse shape deteriorates with higher PRF
Pulse magnitude decreases with increasing PRF. Clockwise from top left: 33, 50, 70, 100 KHz.
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14
Laser intensity
Computation of intensity
-  usually for each received echo
-  computed from echo magnitude, or integration of area of each echo (better)
-  quantisation with 8- to 16-bit
Laser intensity has several problems:
-  Laser footprints with area gaps or overlaps. Footprint has varying irregular shape.
-  Laser is monochromatic source. Some objects may reflect very low to nothing.
-  Noise is high (e.g. 10% estimated using homogeneous surfaces)
-  Intensity for same object may be inhomogeneous (depends on flying height, scan angle
etc.). Thus, needs normalisation within one image and across images for multi-temporal
analysis (see Hoefle and Pfeifer, 2007).
-  Saturation with highly specular surfaces
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Laser intensity
Intensity rarely used. Possible uses:
-  Visualisation (poor quality ; see next slide)
-  Matching of intensity to camera images (e.g. for co-registration)
-  Detection of tie points for laser strip co-registration, or matching of tie with control points
-  Boresight calibration (in-flight) using tie points in intensity images (e.g. used by Leica)
-  Classification of laser points in various object classes (difficult; used more in forestry,
glaciology)
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15
Examples of laser intensity image
Comparison of digital camera (top)
and laser intensity (bottom).
Quality much worse than camera images. Objects with low reflectivity at laser wavelength
or specular objects reflecting away from the sensor or very far objects appear dark.
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Interaction with targets
First echoe from tree canopy
2nd, 3rd etc. echoes from tree branches
Last echoe from ground
Multiple echoes generally with vegetation (semi-transparent objects), also at abrupt
surface discontinuties (e.g. building edges) and overhanging objects (e.g. power lines)
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16
Interaction with targets
AT
t
AR
first pulse
last pulse
t
5m
tp
Usually returned pulse magnitude lower and width wider (not as shown
in figure above)
Assuming returned pulse width of 10 ns  3 m
 Min distance of separable objects Δh = 1.5 m (half the pulse duration)
In theory, in reality minimal vertical separation is larger. A more
realistic measure is: t/2 < Δh < t , i.e. 1.5m < Δh < 3m
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Interaction with targets
AT
t
AR,1
t1
Detection accuracy ≈ 10-15% t
of rise time ≈ 3 - 4.5 cm, for
t rise = 1 ns
For range estimation, usually the leading edge of the sent pulse and
each received echo is used. For edge detection some use a fixed
threshold (poor method), others select the threshold at half the rise
time (time corresponding to half amplitude).
For flat surfaces with good homogeneous reflectivity in the laser
footprint, received pulse very similar to sent one -> small rise time
(good range accuracy), no range averaging of various targets.
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17
Interaction with targets
AT
t
AR,2
t2
t rise
7 ns
Multiple irregular surfaces close to each other reflect an
incoming pulse. The reflected pulses are combined to a
wider pulse with lower magnitude and longer rise time
-> lower range accuracy, range averaging
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35
Interaction with targets
(same reflectivity as left
but for sloped terrain)
•  Measured range depends on surface slope and roughness, e.g. for the 2 left figures return
pulse on the right is wider than on the left and measured range is an average of the range of
the laser footprint
•  Minimum detectable object size depends on reflectivity (e.g. thin power cables are detectable).
The returned pulse on the 2 right figures may be detectable if the yellow area has high
reflectivity, even if it covers a small area within the laser footprint
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18
Interaction with targets
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Tree penetration - Occlusions
Canopy penetration decreases with increasing scan
angles and increasing leaves (for deciduous trees).
Typical values with laser profiler (with leaves): 30-40%
coniferous, 20-25% deciduous (> 60% in winter).
Similarly, occlusions increase with increasing scan
angle (important for 3D building and city modeling).
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19
ground
bush
tree crown
AR
power line
Echoes - Conventional ALS
t
t1
First
echoe
t2
2nd
echoe
t3
3rd
echoe
Data
recorder
t4
Last
pulse
Above: returned full continuous waveform, but only 4
discrete echoes are registered.
First conventional ALS registered first, last or both
echoes. Current conventional ALS register 4-5
echoes.
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ground
bush
tree crown
AR
power line
Echoes - Full waveform ALS
t
First echoe
Last echoe
AR
1 Giga-samples/s
t
Above: returned full continuous waveform
Data
recorder
Below: discrete sampling of continuous waveform.
Unnecessary data (e.g. when only one echo) are discarded.
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20
Conventional versus full waveform digitising
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Example of full waveform digitising – Tree profile
vertical profile of raw full-waveform ALS data
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21
Multiple Pulses in Air (MPiA) technology
•  Introduced by Leica in 2006
–  Send next pulse before receiving the previous one
–  Allows Laser system to operate at double the pulse rate of current Leica systems at any given
altitude ( from 1000m?) up to 5,000 m?
•  Used also by other major ALS manufacturers (Optech, Riegl) with other terms than MPiA,
•  Other possibilities for multiple pulses
- use two ALS systems, sharing IMU and some electronic parts
- splitting laser pulse in two, one at nadir, one slightly off (mentioned in literature for Leica ALS
but implemented)?
•  Benefits
–  Double the data density at current swath
–  Double the swath at current density
–  Data acquisition cost savings
Problem with sending 2 pulses before receiving one, in steep terrain.
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MPiA: Single-pulse technology limits pulse rate
2
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3
1
4
44
22
MPiA allows doubling of pulse rate
1
2
3
2
3
4
4
5
5
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Other multiple pulse technology (used by Optech)
First consider „What influences maximum flying height ?“
1. 
The so-called timing limit
- Normally one pulse must be received before the next is transmitted
- This poses restrictions to the PRF in relation to flying height, i.e.
PRF must be decreased with higher flying height.
- Example:
- Assume 100kHz PRF
- This leaves 10 microseconds between 2 consecutive laser shots
- In 10 microseconds light travels 3 km. Dividing by 2 (travel back and forth)
gives 1.5 km flying height. Accounting for atmospheric interference actual
limit is closer to 1.1 km.
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23
What influences maximum flying height ?
2. 
The received signal strength is influenced by A) the PRF and B) the sensitivity of the
laser detector (including noise level), assuming a given object reflectance.
A) Normally the strength (amplitude) decreases with increasing PRF
almost linearly
Leica has developed a technology where this does not occur.
B) The received power decreases with the SQUARE of the distance to the object
In most ALS systems, the timing limit occurs before the signal strength limit.
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Other multiple pulse technology (used by Optech)
For most ALS systems, the timing limit occurs before the signal strength limit. A
technology where both limits are similar is the Optech multipulse technology. A pulse can
be recorded before the next is sent.
According to informal sources, the Optech
multi-pulse technology uses range gating
and tracking. Some tests at Swisstopo with
such a system have led to many data
errors, occuring at abrupt and large
surface discontinuities.
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24
Error sources in 3D point estimation
•  Range errors: due to clock errors, low reflection/large range, double bounce (called also multi-path,
leading to longer range), rough or discontinuous surfaces, secondary received reflections (sun,
clouds), atmospheric attenuation, detector and electronic errors.
•  Errors in laser beam deflection (e.g. mirror angle measurement) or deflection mechanism calibration
•  DGNSS (receiver type, satellite constellation and topography, ground reference constellation and
distance to aircraft, frequency, satellite signal disturbances)
•  IMU (accuracy, frequency, drift)
•  Calibration values between GNSS, IMU, laser scanner (offsets, alignment)
•  Dynamic bend of IMU / scanner mounting plate, effects from temperature, pressure, humidity
•  Missing or wrong in-flight calibration
•  Method and software to combine GNSS, IMU and calibration data to estimate position and orientation
of each laser pulse
•  Time synchronization and interpolation (Frequency: .g. GNSS: 1-10/s, IMU 200-500/s, Laser finder
very hight, e.g. 100,000/s ; interpolation errors increase with decreasing GNSS/IMU frequency and
more turbulent flight)
•  Transformation to local map coordinate system
Etc.
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Error budget (geometry)
Simulation using only some error sources
Z
κ
Δκ
ϕ
ΔX0, ΔY0, ΔZ0
ω
h
Δβ
Δϕ
Δω
Y
ΔR
ΔZ
ΔY
X
ΔX
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25
due
to
error
in
Error budget (geometry)
β
Δω
Δϕ
0
15
ΔX
Δκ
Total
Total
@ h=1000m
22.4
53.0
23.6
56.2
27.6
66.6
26.4
63.5
26.4
63.5
2.5
26.5
63.6
0
5
9.4
9.4
4
5
11.7
19.1
8
4
17.0
37.3
Δβ
0
20.9
7.5
0
ΔZ0
0
8
0
0
0
20.9
0
0
14
30
ΔZ
ΔY0
16.1
0
15
ΔX0
0
30
ΔY
ΔR
0
0
15
5.6
30
12.1
0
0
1.3
0
8
0
0
0
Assumptions: h = 400 m (except last column h = 1000 m), ω = ϕ = κ =0,
Δω = Δϕ = 0.03°, Δκ = 0.04°, Δβ = 0.02°,
ΔR = 5 cm, ΔX0 = ΔY0 = ΔZ0 = 8 cm
8
cm
0
0-5 5-10 10-15 15+
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error
in
51
Error budget: conclusions
β
Δω
Δϕ
Δκ
0
20.9
7.5
0
15
ΔX
Total
Total
@h=1000
22.4
53.0
23.6
56.2
27.6
66.6
26.4
63.5
26.4
63.5
2.5
26.5
63.6
0
5
9.4
9.4
4
5
11.7
19.1
8
4
17.0
37.3
Δβ
ΔR
ΔX0
ΔY0
ΔZ0
0
0
8
0
0
0
30
16.1
0
15
ΔY
0
20.9
0
0
14
30
ΔZ
0
0
15
5.6
30
12.1
0
0
1.3
0
0
8
0
0
8
•  ΔY slightly larger than ΔX for small β (due to Δβ error)
•  Above relation changes for larger β (due to Δκ error)
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26
due
to
error
in
Error budget: conclusions
β
Δω
Δϕ
0
15
ΔX
Δκ
Δβ
0
20.9
7.5
0
ΔZ0
0
8
0
0
0
20.9
0
0
14
30
ΔZ
ΔY0
16.1
0
15
ΔX0
0
30
ΔY
ΔR
1.3
0
8
0
2.5
0
0
15
5.6
30
12.1
0
0
0
5
4
5
8
4
0
0
8
Total
Total
@h=1000
22.4
53.0
23.6
56.2
27.6
66.6
26.4
63.5
26.4
63.5
26.5
63.6
9.4
9.4
11.7
19.1
17.0
37.3
•  ΔR has only marginal influence on ΔZ
•  And almost no influence on ΔX, ΔY
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due
to
error
in
53
Error budget: conclusions
β
Δω
Δϕ
Δκ
0
20.9
7.5
0
15
ΔX
Total
Total
@h=1000
22.4
53.0
23.6
56.2
27.6
66.6
26.4
63.5
26.4
63.5
2.5
26.5
63.6
0
5
9.4
9.4
4
5
11.7
19.1
8
4
17.0
37.3
Δβ
ΔR
ΔX0
ΔY0
ΔZ0
0
0
8
0
0
0
30
16.1
0
15
ΔY
0
20.9
0
0
14
30
ΔZ
0
0
15
5.6
30
12.1
0
0
1.3
0
0
8
0
0
8
•  ΔZ smaller than ΔX, ΔY and less dependent on h
Reason: for ΔZ, ΔR, ΔZ0 dominate and are nearly independent of h
•  ΔZ mainly depends on ΔZ0 (GNSS!) (and ΔR) for small β
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27
due
to
error
in
Error budget: conclusions
β
Δω
Δϕ
0
15
ΔX
Δκ
Δβ
0
20.9
7.5
0
ΔZ0
0
8
0
0
0
20.9
0
0
14
30
ΔZ
ΔY0
16.1
0
15
ΔX0
0
30
ΔY
ΔR
1.3
0
8
0
2.5
0
0
15
5.6
30
12.1
0
0
0
5
4
5
8
4
0
0
8
Total
Total
@h=1000
22.4
53.0
23.6
56.2
27.6
66.6
ΔZ
26.4
63.5
26.4
ΔX, ΔY
26.5
63.5
63.6
9.4
9.4
11.7
19.1
17.0
37.3
•  ΔZ given is too optimistic
•  Especially for sloped terrain, ΔX, ΔY dominate and cause also height errors
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PiA = Pulse in the Air
1PiA: one pulse in the
air
2PiA: two pulses in the
air
-  Z- accuracy better than XY (espec. as height increases). Both deteriorate with height, Z only a little.
-  Accuracy worse at FOV edge than at nadir. Accuracy deteriorates with higher PRF.
-  2PiA more accurate than 1PiA for same height and PRF. Or provides same accuracy for higher height.
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28
Accuracy of ALS
Main products:
-  raw laser point cloud (how close is laser point to „true“ position/footprint)
-  classification of point clouds (degree of correctness (e.g. probabiliy) of classification)
Derived products
-  geometric (DTM, DSM etc.)
-  thematic/semantic (e.g. object detection and classification)
DTM accuracy
- Difference of DTM to „true“ surface (differences between measured check points and their interpolated
height in DTM). Difficulties: surface modelling errors, surface roughness
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Accuracy of ALS
DTM accuracy
Empirical accuracy will deteriorate when surface is poorly defined (rough) and laser point density is
decreased (bigger interpolation errors).
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29
Precision of ALS
A priori estimation of point precision based on error assumptions and error propagation.
Can help decide on optimum data acquisition parameters.
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Empirical accuracy - Summary
-  Accuracy given often as 1 sigma
-  Companies give own empirical formulas and/or graphs
-  Z- accuracy better than XY (espec. as flying height increases ; better by a factor 2-5). Both deteriorate
with flying height, Z only a little.
-  Accuracy worse at strip edge than at nadir.
- Accuracy (DTM) worse with smaller tree penetration rate.
-  Z-accuracy worse with increasing terrain slope. And at abrupt surface discontinuties (building borders).
- Accuracy deteriorates slightly with higher PRF.
-  For grid intepolation, Z depends on density of raw data, grid spacing and interpolation quality. Grid
spacing should be ideally larger (≥ 2) than average point distance of raw points.
-  Typical Z-accuracies on undulating bare terrain for up to 3,000 flying height: 5-20 cm
-  For some detailed investigations on DEM quality and accuracy espec. related to ALS see Kraus et al.
(2004, 2006), Karel et al. (2006), Karel and Kraus (2007).
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ALS Data Acquisition and Processing Workflow
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Coarse Processing Flow (first stages to produce a point cloud)
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31
Processing flow (example Leica and Terrasolid software)
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Calibration
•  Very important for high accuracy. Can include:
-  Factory tuning and fixing of values
Intensity-based range correction (performed by Leica: bright/dark objects reflect faster/
slower, range is shorter/longer), other range offset calibrations
Scanner encoder offset / scan angle correction (can include scale correction)
Electronic components (several components tested/tuned and their values fixed)
-  Mounting of sensors on aircraft (misalignment, offsets between GNSS/IMU and laser)
-  In-flight calibration
Misalignment, offsets, range and angle offsets and scale (mostly refined in strip
adjustment)
Calibration not standardized, often company specific.
For some details, see presentation of Yuji Kuwano “System Overview” in the Kanpur
Laser Scanning tutorial and P. Friess at the Ljubljana tutorial (see references at the end).
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32
GNSS / IMU data processing
•  Combined adjustment of GNSS / IMU data - Kalman filtering
•  Combination with offsets / misalignment to derive orientation for each laser pulse
•  Some of the above parameters refined during the strip adjustment
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Strip adjustment
Create a seamless data set by correcting for
systematic errors between strips
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Check relative strip orientation
Color coded DEM differences in strip overlap before and after strip adjustment
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Absolute orientation check after strip adjustment
With respect to a map coordinate system. Easy check by overlaying accurate vector map data
Wrong absolute orientation
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34
Absolute orientation check after strip adjustment
With respect to a map coordinate system. Easy check by overlaying accurate vector map data
Correct absolute orientation
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Filtering – Introduction
•  Digital terrain model (DTM): “ground”
•  Digital surface model (DSM): “top visible surface”
•  Digital elevation model (DEM): here used as both DSM and DTM
•  Filtering: classification of points into terrain and above-terrain (sometimes with
separation to buildings and trees ; few other non-terrain objects may exist)
•  Basis for DSM and DTM generation, detection of above-terrain objects (mainly
buildings and trees) by subtracting (DSM - DTM) = normalised DSM (nDSM).
Special case Canopy Height Model (CHM): in nDSM remove buildings and other
non tree/bush objects.
Other related important issues:
-  Intelligent data thin-out (huge datasets!)
-  Automated detection of breaklines
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35
Filtering – Introduction
DSM
DTM
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Filtering – Introduction
DSM
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Normalised DSM (nDSM) = DSM - DTM
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36
Filtering – Introduction
Canopy Height Model (CHM) = nDSM - all non-tree objects (technical constructions, rocks).
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Filtering – Introduction
DTM
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DTM improved with breaklines
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37
Filtering
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Filtering
Connection of neighbouring points does not produce a meaningful surface.
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38
Filtering
Points that do not
belong to DSM or
DTM, which have to
be eliminated
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Filtering – Introduction
Various aims:
•  Filtering to extract ground points
•  Filtering to extract ground
surface
•  Classification to label points
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39
Filtering results
Left: raw point density 5.6 points/m2 (top), reduced to 1 point/16m2 (bottom).
Right (DTM after filtering): Filtering errors increase with lower point density.
Filtering: compromise between good elimination of non-terrain objects and preservation of terrain details.
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Filtering results
Large building
(railway station)
Part of Stuttgart (above raw data, below DTM). Errors increase with size of non-terrain objects,
terrain slope (when buidlings/trees) and scene complexity (dense or overlapping buildings/trees).
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Filtering results
Classification sometimes to: ground objects (red), vegetation (yellow) AND other above-terrain
objects (cyan), or buildings and other above-terrain objects (e.g. in SCOP++ Lidar, Inpho)
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Filtering results
DTM raw data of Swisstopo (Zurich airport), right zoom. Problems to interpolate
DTM, when large non-terrain objects are present, and terrain not flat.
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ISPRS laser data filtering test
Comparison of filter algorithms, 2002-2004
•  8 sites, 8 participants
•  Qualitative and quantitative evaluation
Conclusions:
•  All filters perform well on smooth terrain with vegetation and buildings. All filters
have problems with rough terrain and complex city landscapes.
•  In general, filters that compare points to locally estimated surfaces performed best.
•  The problems caused by the scene complexities were larger than those caused by
the reduced point density.
•  Research on segmentation, quality assessment and usage of additional
knowledge sources is recommended.
•  Full report on http://www.itc.nl/isprswgIII-3/filtertest/index.html
Commercial software (see below) can have, depending on scene complexity,
90-95% success rate. The rest is corrected partly automatically (for large errors),
partly semi-automatically or manually using visualisation techniques, overlay with
images and maps etc. Very important: how good / fast are software tools for
manual editing!
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More details on filtering
Input data: raster grid (simpler processing), Triangular Irregular Network (TIN) (original points
are used, avoids interpolation errors).
dy
dx
Raster
TIN
•  Basic approaches:
•  Mathematic morphology
•  Slope-based filtering (can be implemented as morphological operation)
•  Progressive densification
•  Surface-based (robust interpolation)
•  Segmentation (can actually be implemented with various filtering methods)
•  Point(s) / local neighbourhood versus segments
•  Problems: selection of thresholds, selection or local neighbourhood size, treatment of large
objects, mixed objects, low objects on terrain, bridges/overpasses, preservations of
discontinuities
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Test neighborhood and the number of points filtered at a time
Point-to-Point:
•  Two points are compared at a time.
•  Based on the positions of the two points.
•  Only one point can be classified at a time.
Point-to-Points:
Points-to-Points:
Measure of Discontinuity
–  Most algorithms classify based on some measure of discontinuity.
–  Some of the measures of discontinuity used are, height difference, slope, shortest distance to
TIN facets, and shortest distance to parameterised surfaces.
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Slope Based
Filtering with surfaces
Block Minimum
Surface based
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43
Mathematical Morphology (binary values)
–
s = 2D structuring element
(like filter mask)
=
- = Erosion operation
+ = Dilation operation
I
s
+
I-s
I-s
=
s
Opening = Erosion,
then Dilation
I o s := ( I − s ) + s
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Mathematical Morphology (grey values)
DSM
Erosion (local minimum)
Structuring Element (SE)
Dilation (local maximum)
DTM
Opening
Possible modifications of conventional morphological filtering: variable SE size, use with irregular points,
use of rank filters instead of min and max, use of multiple iterations.
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Mathematical Morphology - Example of opening
Original DSM
The larger the SE, the
stronger the filtering of
above-terrain objects, but
also the smoothing of terrain
details.
11x11 m2
15x15 m2
21x21 m2
31x31 m2
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Slope-based Filtering
Filter kernel showing the
max. permissible slope
89
Slope as max permissible height
difference as function of distance d.
Δhmax (d )
d
Point with high slope
is eliminated from DTM
Δhmax (d )
d
DTM
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Account for measurement noise
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45
Slope-based Filtering - Example
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TIN densification
(implemented in TerraScan software, Terrasolid)
•  Start using sparse seed points
•  lowest points in a large grid, based on largest structure, e.g. 50-100 m
•  Densify iteratively from below
•  calculate required thresholds from points currently included in the TIN
•  add points to the TIN, if they are within thresholds
•  Threshold computation based on median values of surface normal angles and
elevation differences. Uses histograms for computation of median.
α
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TIN densification
•  Add one point at a time in each triangle facet
•  Accept based on distance and angle threshold
•  Special case for discontinuous surfaces (urban areas -> buildings)
•  Then, usual threshold values easily exceeded
•  Use mirroring of examined point at closest point in TIN triangle to compute deviation
d
γ
d
α
mirror
d´
β
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Hierarchical Robust Interpolation
(implemented in SCOP++ Lidar software, Inpho)
- 
Filtering -> here hierarchical robust interpolation (based on Kriging) with an eccentric
and asymmetrical weight function:
1.  Optionally remove buildings (in Inpho software)
2.  Remove trend surface (low-order polynomial)
3.  Do interpolation and filtering
4.  Perform robust interpolation (or if clusters of blunders exist, do it hierarchically).
Requires good mixture of terrain and above-terrain points
5.  Create DEM pyramids with decreasing resolution (Thin-out) (often 3 levels suffice)
6.  Generate a coarse initial DEM (Interpolation)
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Hierarchical Robust Interpolation
(implemented in SCOP++ Lidar software, Inpho)
7. (Filtering) Classify points ( on and above ground) and apply robust interpolation to
generate a DEM, starting at the coarsest level, using an asymmetric weight
function:
•  low weights assigned to points that are significantly above the terrain
•  high weights assigned to points on or below the approximated DTM
8. (Sort-Out) Accept in the original data only points lying within a band of this
interpolated DEM (+/- 3 sigma of height accuracy of input points).
Steps 6-8 are repeated for each finer pyramid level -> Output DSM (buildings, other
non-terrain points) and DTM
Consideration of breaklines, if available, for improving DTM quality.
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Robust interpolation
•  Generation of a low resolution data pyramid using
the original data (e.g. xyz-coordinates of the
lowest points in a grid)
•  Computation of a low resolution DTM using
robust interpolation along with blunder detection
•  Elimination of LIDAR points outside a predefined
tolerance band (here +/- 1 m)
•  Computation of a DTM with full resolution using
robust interpolation along with blunder detection
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Interpolation and Filtering - Data pyramid
- Compute data (point set) at different levels:
Select within each cell, lowest or mean point or
closest to cell center
- Process (filter) different levels from coarse to
fine,
e.g. 20m grid -> 4m grid -> original data
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Interpolation and Filtering - Principle
kkahhmm
kkahhmm
Left: interpolation. Function passes through values of input data (considered error-free).
Right: Interpolation and filtering. Function does not pass through values of input data,
because a Z measurement error is considered.
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Interpolation and Filtering
•  Initial interpolation using unit weights (same weight
for all points i) : σi2 = σ02, with σ02 a priori accuracy of points
Outlier, probably vegetation
Interpolation follows
outlier
Result of interpolation +
filtering
(Source: I.P.F. TU Vienna)
Trend surface
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Robust interpolation
•  Calculate filter values = oriented distance from measured point to surface
•  Compute histogram of filter values
•  Asymmetric (different weight function left and right of origin): many points above
surface, few points below surface
•  Eccentric: origin is not at 0 but at g
(Source: I.P.F. TU Vienna)
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50
Robust interpolation
•  Use asymmetric weight function (a,b different for left & right branch)
pi =
1
b
1 + (a ⋅ f i − g )
, σ i2 =
σ 02
pi
•  a, b parameters, g shift determined from histogram
•  Also remove points which are too far off the surface (both positive and negative): cut-off, tolerance
Cut-off
(Source: I.P.F. TU Vienna)
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Robust interpolation
Initial
interpolation
Refined (robust)
interpolation
(Source: I.P.F. TU Vienna)
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51
Robust interpolation
Weight function p
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Robust interpolation
Weight parameters (and some other parameters) change from pyramid level to level
5m level
select mean point in 5m x 5m cell
robust filtering
weight function half weight @75cm
weight function tolerance 1m
select Points ±3m of DTM
2m level
select lowest point in 2m x 2m cell
robust filtering
weight function half weight @30cm
weight function tolerance 60cm
select Points ± 2m of DTM
original (0.5m level)
robust filtering
weight function half weight @20cm
weight function tolerance 30cm
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Robust interpolation
Pyramid level 1 (lowest resolution). In each grid cell, the lowest point is selected.
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Robust interpolation
Pyramid level 2
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Filter results in forested area
(Source: I.P.F. TU Vienna)
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ALS raw data filtering - Various predefined strategies (SCOP++ Lidar)
From left: weak, default, strong strategy. Compare differences regarding above-terrain object
filtering, espec. Between strong and other strategies.
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ALS raw data filtering - Different filter parameter values (SCOP++ Lidar)
The filter parameter for this software determines the compromise between elimination of above-terrain
objects and preservation of terrain details. Strategy used is default. From left, filter parameter values:
2, 1, 0.5.
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Segmentation-based Filtering
Problem analysis of many filters
•  Smooth surface assumption does not hold
•  Lack of context information
–  Filtering applied only locally
–  Point-wise filtering
New filter approach
•  Use continuous surfaces instead of smooth surfaces
•  Filter continuous segments of points instead of points
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Segment based filtering
•  Texture based image segmentation
•  Point cloud segmentation into continuous surfaces
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Slide provided by George Sithole, George Vosselman
111
Profile segmentation
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Delaunay
Triangulation
Minimum
Spanning Tree
Proximity
Thresholding
Remove
Dangling Edges
112
56
Profile classification
Raised
Lowered
Terraced
High
Low
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Combining profiles to segments
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57
Combining profiles to segments
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Segment classification
• 
Based on majority of segment profile
classifications
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58
Bridge detection
•  Select all terrain profiles
•  Analyse profile segment classifications
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Conclusions for segment based filtering
• 
• 
• 
Segment-based filtering
–  preserves discontinuities
–  allows filtering of large objects
–  can be combined with other filtering methods
–  could be extended with other attributes (shape, size, colour)
Segmentation in areas with low vegetation remains difficult
Bridges can be recognised in bare earth segment
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