dprg chapters 1 – 7: overview · • lidar data includes ground/terrain and...
TRANSCRIPT
![Page 1: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/1.jpg)
Ayman F. Habib1
DPRG
Laser Scanning
Chapters 1 – 7: Overview• Photogrammetric mapping: introduction, applications, and
tools• GNSS/INS-assisted photogrammetric and LiDAR
mapping• LiDAR mapping: principles, applications, mathematical
model, and error sources and their impact.• QA/QC of LiDAR mapping• Registration of Laser scanning data• Point cloud characterization, segmentation, and QC
• This chapter will be focusing on LiDAR-based orthophoto and Digital Terrain Model (DTM) generation.
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Ayman F. Habib2
DPRG
Laser Scanning
OCCLUSION-BASED PROCEDURE FOR TRUE ORTHOPHOTOGENERATION AND LIDAR DATA CLASSIFICATION
Chapter 8
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Ayman F. Habib3
DPRG
Laser Scanning
Overview• Introduction• Orthophoto generation
– Literature review– Procedure
• LiDAR data classification– Literature review– Procedure– Experimental results
• Concluding remarks
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Ayman F. Habib4
DPRG
Laser Scanning
True Orthophoto Generation
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Ayman F. Habib5
DPRG
Laser Scanning
Image and Map characteristics
object
Image plane
Image
map
Relief displacement
No relief displacement
Non-uniform scale
Uniform scaleOrthogonal projection
Perspective projection
An orthophoto is a digital image which has the same characteristics of a map.
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Ayman F. Habib6
DPRG
Laser Scanning
Perspective Image
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Ayman F. Habib7
DPRG
Laser Scanning
Orthophoto
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Ayman F. Habib8
DPRG
Laser Scanning
Orthophoto Generation: Prerequisites• Digital image:
– Wide range of operational photogrammetric systems• Interior Orientation Parameters (IOPs) of the used
camera:– Camera calibration procedure
• Exterior Orientation Parameters (EOPs) of that image: – Image georeferencing techniques
• Digital Surface Model (DSM) or Digital Terrain Model (DTM)– LiDAR, imagery, Radar, …
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Ayman F. Habib9
DPRG
Laser Scanning
Digital Image
PC
(x, y)
Backward Projection (EOP & IOP)
Datum
Terrain
g(resampling)
G(X, Y) = g (x, y)
Z(X, Y)
Interpolation
(X, Y)
Differential Orthophoto Generation
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Ayman F. Habib10
DPRG
Laser Scanning
Digital Surface Model
perspective center
imagery
AB C
D
Orthophoto
Differential Orthophoto Generation
ghost image/double-mapped area
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Ayman F. Habib11
DPRG
Laser Scanning
Differential Orthophoto Generation
Generated OrthophotoOriginal Imagery
Double-mapped areas
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Ayman F. Habib12
DPRG
Laser Scanning
Digital Image
PC
Indirect (backward) transformation
Orthophoto Generation & Visibility Analysis
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Ayman F. Habib13
DPRG
Laser Scanning
perspective center
imagery
0 01 1 120
AB C
D
a b c
DC P.C
longer
Invisible point
Digital Surface Model
Orthophoto
Z-Buffer Method
True Orthophoto Process – Existing Method
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Ayman F. Habib14
DPRG
Laser Scanning
True Orthophoto Process – Existing Method
Generated True OrthophotoOriginal Imagery
Z-Buffer Method
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Ayman F. Habib15
DPRG
Laser Scanning
True Orthophoto Process – Existing Method
Generated True Orthophoto
Z-Buffer Method
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Ayman F. Habib16
DPRG
Laser Scanning
True Orthophoto Generation
perspective center
A
BInvisible point
DE
C
5 ° visible
12° visible
15° invisible
12 °
15°
14°
A
B
C
D
E 20° 15° visible
max angle
visible/ hiddenpoint angle comparison
0° visible5° >
>
>
<=
>
Nadir point
Digital Surface Model
Orthophoto
Angle-based Method
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Ayman F. Habib17
DPRG
Laser Scanning
00
ii
Radial Sweep for the Angle-Based Method
True Orthophoto Generation
Angle-based Method
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Ayman F. Habib18
DPRG
Laser Scanning
True Orthophoto Gen.: Adaptive Radial Sweep
DSM partitioning for the adaptive radial sweep method
DSM
column
row
nadir pointsection 1
section 2
section 3
1
23
321
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Ayman F. Habib19
DPRG
Laser Scanning
Conceptual procedural flow of the spiral sweep method
DSM
column
row
target point
nadir point
True Orthophoto Gen.: Spiral Sweep
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Ayman F. Habib20
DPRG
Laser Scanning
Comparative Analysis
Z-buffer method
Angle-based (spiral sweep) method
Differential rectification
Angle-based (adaptive radial sweep) method
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Ayman F. Habib21
DPRG
Laser Scanning
Original Image
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Ayman F. Habib22
DPRG
Laser Scanning
LiDAR Surface Model
Elevation Data Intensity Data
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Ayman F. Habib23
DPRG
Laser Scanning
Orthophoto with Ghost Images
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Ayman F. Habib24
DPRG
Laser Scanning
True Orthophoto without Ghost Images
![Page 25: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/25.jpg)
Ayman F. Habib25
DPRG
Laser Scanning
True Orthophoto After Occlusion Filling
![Page 26: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/26.jpg)
Ayman F. Habib26
DPRG
Laser Scanning
perspective center
A
BInvisible point
DE
C
5 ° visible
12° visible
15° invisible
12 °
15°
14°
15°
A
B
C
D
E 20° visible
max angle
visible/ hiddenpoint angle comparison
0° visible5° >
>
>
<=
>
Nadir point
Digital Surface Model
Orthophoto
Occlusion Extension
Angle-based Method
16°
![Page 27: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/27.jpg)
Ayman F. Habib27
DPRG
Laser Scanning
True Orthophoto After Occlusion Filling
![Page 28: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/28.jpg)
Ayman F. Habib28
DPRG
Laser Scanning
True Orthophoto After Occlusion Extension
![Page 29: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/29.jpg)
Ayman F. Habib29
DPRG
Laser Scanning
True Orthophoto After Boundary Enhancement
![Page 30: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/30.jpg)
Ayman F. Habib30
DPRG
Laser Scanning
Orthophoto with Ghost Images
![Page 31: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/31.jpg)
Ayman F. Habib31
DPRG
Laser Scanning
True Orthophoto without Ghost Images
![Page 32: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/32.jpg)
Ayman F. Habib32
DPRG
Laser Scanning
True Orthophoto After Occlusion Filling
![Page 33: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/33.jpg)
Ayman F. Habib33
DPRG
Laser Scanning
True Orthophoto After Occlusion Extension
![Page 34: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/34.jpg)
Ayman F. Habib34
DPRG
Laser Scanning
True Orthophoto After Boundary Enhancement
![Page 35: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/35.jpg)
Ayman F. Habib35
DPRG
Laser Scanning
Classification of LiDAR Data(Ground/Non-Ground Points)
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Ayman F. Habib36
DPRG
Laser Scanning
LiDAR Classification: Introduction• LiDAR data includes ground/terrain and non-ground/off-
terrain points.– Knowledge of the terrain is useful for deriving contour lines,
road network planning, and flood monitoring.– Knowledge of the off-terrain points is useful for DBM detection,
DBM reconstruction, 3D city modeling, and 3D visualization. – Knowledge of terrain and off-terrain points is useful for change
detection applications.
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Ayman F. Habib37
DPRG
Laser Scanning
LiDAR Classification: Introduction• Definition of ground/non-
ground (Sithole & Vosselman, 2003)– Ground: Topsoil or any thin
layering (asphalt, pavement, etc.) covering it
– Non-ground: Vegetation and artificial features
• How to distinguish ground points from non-ground points in LiDAR data?
Ground Profile
Non-Ground Profile
LiDAR Profile
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Ayman F. Habib38
DPRG
Laser Scanning
LiDAR Classification: Literature• Categories (Sithole & Vosselman 2003):
– Slope-based– Block-minimum– Surface-based– Clustering/segmentation
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Ayman F. Habib39
DPRG
Laser Scanning
LiDAR Classification: Literature Review• Modified Block Minimum (Wack and Wimmer, 2002)• Modified Slope-based Filter (Vosselman, 2000)• Morphological Filter (Zhang et al., 2003)• Active Contour (Elmqvist et al., 2001)• Progressive TIN Densification (Axelsson, 2000)• Robust Interpolation (Pfeifer et al., 2001)• Spline Interpolation (Brovelli et al., 2002)
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Ayman F. Habib40
DPRG
Laser Scanning
LiDAR Classification: Concept• Assumption: Non-ground
objects produce occlusions in synthesized perspective views.
• Search for occlusions Non-ground objects can be detected as those causing occlusions.
Perspective Projection
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Ayman F. Habib41
DPRG
Laser Scanning
LiDAR Classification: Processing Flow
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Ayman F. Habib42
DPRG
Laser Scanning
LiDAR Classification: Methodology• LiDAR data is irregularly
distributed. • We start by interpolating the
LiDAR data.– The average point density is
used to estimate the optimum GSD for resampling.
– We use the nearest neighbor interpolation to avoid blurring the height discontinuities.
Point C
Point A
Point B
The jthColumn
The ith
Row
DSM( i , j ) = Height of Point B
![Page 43: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/43.jpg)
Ayman F. Habib43
DPRG
Laser Scanning
LiDAR Classification: Methodology• If there is more than 1 point located in a given cell, we
pick the one with the lowest height and assign its height to that cell.
Point A
Point B
Point CThe jthColumn
The ith
Row
DSM( i , j ) = Height of Point C
![Page 44: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/44.jpg)
Ayman F. Habib44
DPRG
Laser Scanning
LiDAR Classification: Methodology
• Occlusion Detection(Angle-based)
A
C
PC
Nadir Point
Off-Nadir Angle
A B C D E
B
D
E
DE E: Occlusion!!!
AB
Visible Point (B)
BC
CD
Visible Point (C)
Visible Point (D)
Last Visible Point (D)
First Occluded Point (E)
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Ayman F. Habib45
DPRG
Laser Scanning
LiDAR Classification: Methodology
PC
Last Visible Point
First Occluded Point
D E
E
Nadir Point
D
C
• Detect the Points Causing Occlusion
C
B
EC
EB
C: Non-Ground
D: Non-GroundB: Ground
B
Non-Ground
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Ayman F. Habib46
DPRG
Laser Scanning
LiDAR Classification: Methodology• How can we maximize our
ability to detect the majority of non-ground objects?– Manipulate the location &
number of synthesized projection center(s)
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Ayman F. Habib47
DPRG
Laser Scanning
LiDAR Classification: Methodology
• Non-ground points detected from projection centers with different horizontal locations
![Page 48: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/48.jpg)
Ayman F. Habib48
DPRG
Laser Scanning
• Non-ground points detected from projection centers with different vertical locations
LiDAR Classification: Methodology
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Ayman F. Habib49
DPRG
Laser Scanning
LiDAR Classification: Methodology
• Two opposite projection centers will allow for the detection of a larger non-ground area
PC A
Detected Non-Ground Points From PC A
Detected Non-Ground Points From PC B
Combined Results
PC B
![Page 50: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/50.jpg)
Ayman F. Habib50
DPRG
Laser Scanning
LiDAR Classification: Methodology
The eight neighbors of any given pixel are checked to see if they are occluded by that pixel or not.
![Page 51: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/51.jpg)
Ayman F. Habib51
DPRG
Laser Scanning
A
PC1
For Pixel A
PC 2For Pixel A
PC 3For Pixel A
PC 4For Pixel A
PC 8For Pixel A
PC 7For Pixel A
PC 6For Pixel A
PC 5For Pixel A
dd
d
d
B
LiDAR Classification: Methodology
![Page 52: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/52.jpg)
Ayman F. Habib52
DPRG
Laser Scanning
LiDAR Classification: Methodology
A
PC 1For Pixel B
PC 2For Pixel B
PC 3For Pixel B
PC 4For Pixel B
PC 8For Pixel B
PC 7For Pixel B
PC 6For Pixel B
PC 5For Pixel B
dd
d
d
B
![Page 53: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/53.jpg)
Ayman F. Habib53
DPRG
Laser Scanning
The eight neighbors of any given pixel are checked to see if they are occluded by that pixel or not.
A
Perspective Center 1
For Pixel A
Perspective Center 2
For Pixel A
Perspective Center 3
For Pixel A
Perspective Center 4
For Pixel APerspective
Center 8For Pixel A
Perspective Center 7
For Pixel A
Perspective Center 6
For Pixel A
Perspective Center 5
For Pixel A
dd
d
d
45 Degree
B
LiDAR Classification: Methodology
![Page 54: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/54.jpg)
Ayman F. Habib54
DPRG
Laser Scanning
LiDAR Classification: Results
Simulated Dataset DSM Identified Occluding Points (in white)
Simulated DatasetMisclassified ground points
![Page 55: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/55.jpg)
Ayman F. Habib55
DPRG
Laser Scanning
LiDAR Classification: Methodology• Multiple projection centers at pre-specified locations will:
+ Improve our capability of detecting non-ground points• Useful when dealing with large and low buildings
– Enhance the noise and high-frequency components of the terrain• Will lead to false hypotheses regarding instances of non-ground points
• Solution: implement a statistical filter to refine the occlusion-based terrain/off-terrain classification procedure
![Page 56: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/56.jpg)
Ayman F. Habib56
DPRG
Laser Scanning
LiDAR Classification: Methodology• Points producing occlusions (hypothesized off-terrain
point):– True non-ground points + false non-ground points
• Points not producing occlusions (hypothesized terrain point):– True ground points + false ground points
DSMIdentified Occluding Points (in white)
Less probable
![Page 57: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/57.jpg)
Ayman F. Habib57
DPRG
Laser Scanning
LiDAR Classification: Filtering• We designed a statistical filter to remove the effects of
terrain roughness (e.g., noise in the LiDAR data and high frequency components of the surface – cliffs).
• The elevation “h” of the ground points can be assumed to be normally distributed with a mean “μ” and standard deviation “σ”.
Height
Freq
uenc
y
![Page 58: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/58.jpg)
Ayman F. Habib58
DPRG
Laser Scanning
GroundThreshold : Threshold for modifying non-ground points
groundNonThreshold : Threshold for modifying ground points
OutlierThreshold : Threshold for detecting low outliers
Ground Non-Ground
OutlierThreshold
groundNonThreshold
GroundThreshold
Outliers
LiDAR Classification: Filtering• For each DSM cell, we define a local neighborhood that is
adaptively expanded until a pre-defined number of terrain points is located.– Derive a histogram of the terrain point elevations
Height
Freq
uenc
y
![Page 59: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/59.jpg)
Ayman F. Habib59
DPRG
Laser Scanning
LiDAR Classification: Filtering
• Examples of outliers: multi-path errors, errors in the laser range finder
![Page 60: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/60.jpg)
Ayman F. Habib60
DPRG
Laser Scanning
Ground Non-Ground Ground Non-Ground
GroundTARGETHeight
groundNonTARGETHeight
groundNonTARGETHeight
GroundTARGETHeight
GroundTARGETHeightKeep it
groundNonTARGETHeight
GroundTARGETHeight
OutlierTARGETHeight
Outlier
OutlierThreshold
HeightHeightFreq
uenc
yLiDAR Classification: Filtering
![Page 61: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/61.jpg)
Ayman F. Habib61
DPRG
Laser Scanning
LiDAR Classification: Point Cloud Class.• If a cell is classified as
non-ground, all the LiDAR points in that cell are classified as non-ground points.
• If the cell is classified as a ground point, then– The lowest LiDAR point
in that cell is classified as ground.
– The LiDAR points that are at least 20 cm higher than the lowest LiDAR point are classified as non-ground points.
Point A
Point B
Point CThe i th Column
The j th Row
DSM( i , j ) = Height of Point C
Point C
(Ground)
20cmPoint B Ground
Point A Non-ground
![Page 62: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/62.jpg)
Ayman F. Habib62
DPRG
Laser Scanning
LiDAR Classification: Results
DSM
Classification Results using filterClassification Results without filter
Simulated Dataset
![Page 63: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/63.jpg)
Ayman F. Habib63
DPRG
Laser Scanning
LiDAR Classification: Results
Real Dataset (1 - Brazil)
![Page 64: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/64.jpg)
Ayman F. Habib64
DPRG
Laser Scanning
LiDAR Classification: Results
Real Dataset (1 - Brazil)
Occluding points in white
![Page 65: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/65.jpg)
Ayman F. Habib65
DPRG
Laser Scanning
LiDAR Classification: Results
Real Dataset (1 - Brazil)
After Statistical Filtering
![Page 66: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/66.jpg)
Ayman F. Habib66
DPRG
Laser Scanning
LiDAR Classification: Results
DSM → Non-ground objects
Real Dataset (1 - Brazil)
![Page 67: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/67.jpg)
Ayman F. Habib67
DPRG
Laser Scanning
LiDAR Classification: Results
• Using the LiDAR DSM and an orthophoto over the same area, we manually generated a ground truth for ground and non-ground points classification.
• Comparing our result with the ground truth, the number of misclassified points divided by the total number of points was found to be 4.7%.
Real Dataset (1 - Brazil)
![Page 68: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/68.jpg)
Ayman F. Habib68
DPRG
Laser Scanning
LiDAR Classification: Results
Misclassified Points Misclassified Points displayed on DSM
Real Dataset (1 - Brazil)
![Page 69: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/69.jpg)
Ayman F. Habib69
DPRG
Laser Scanning
LiDAR Classification: Results
Real Dataset (1 - Brazil)
Original DSM Derived DTM
![Page 70: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/70.jpg)
Ayman F. Habib70
DPRG
Laser Scanning
LiDAR Classification: Results
DSM Occluding Points Non-ground Points
Discontinuous Terrain: Tunnels
Real Dataset (2 - Stuttgart)
![Page 71: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/71.jpg)
Ayman F. Habib71
DPRG
Laser Scanning
DSM
Occluding Points
Non-ground Points
LiDAR Classification: Results
Real Dataset (2 - Stuttgart)
![Page 72: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/72.jpg)
Ayman F. Habib72
DPRG
Laser Scanning
LiDAR Classification: Results
DSM Occluding Points Non-ground PointsReal Dataset (2 - Stuttgart)
![Page 73: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/73.jpg)
Ayman F. Habib73
DPRG
Laser Scanning
LiDAR Classification: Results
• A ROI near the University of Calgary is selected as an experimental data.
• The Transit Train trail extends into a tunnel under the ground.
Real Dataset (3 - Calgary)
![Page 74: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/74.jpg)
Ayman F. Habib74
DPRG
Laser Scanning
LiDAR Classification: Results
Non-ground points (TerraScan) Non-ground points (Occlusion-based)
Real Dataset (3 - Calgary)
![Page 75: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/75.jpg)
Ayman F. Habib75
DPRG
Laser Scanning
LiDAR Classification: Results
TerraScan’s Result
Occlusion-Based Result
Real Dataset (3 - Calgary)
![Page 76: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/76.jpg)
Ayman F. Habib76
DPRG
Laser Scanning
LiDAR Classification: Results
• Another ROI near the University station is selected as another experimental data.
• Complex contents – The Transit Train station,– Bridge,– Ramps, and– Trees.
Real Dataset (3 - Calgary)
![Page 77: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/77.jpg)
Ayman F. Habib77
DPRG
Laser Scanning
Non-ground points (TerraScan) Non-ground points (Occlusion-Based Results)
LiDAR Classification: Results
Real Dataset (3 - Calgary)
![Page 78: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/78.jpg)
Ayman F. Habib78
DPRG
Laser Scanning
LiDAR Classification: Results
TerraScan’s Result
Occlusion-Based Result
Real Dataset (3 - Calgary)
![Page 79: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/79.jpg)
Ayman F. Habib79
DPRG
Laser Scanning
Original LiDAR Points over UofC
LiDAR Classification: Results
Real Dataset (4 - Calgary)
![Page 80: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/80.jpg)
Ayman F. Habib80
DPRG
Laser Scanning
Aerial Photo over UofC
LiDAR Classification: Results
Real Dataset (4 - Calgary)
![Page 81: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/81.jpg)
Ayman F. Habib81
DPRG
Laser Scanning
Ground/Non-Ground Points
Real Dataset (4 - Calgary)
LiDAR Classification: Results
![Page 82: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/82.jpg)
Ayman F. Habib82
DPRG
Laser Scanning
LiDAR Classification: Conclusion• The achieved results proved the feasibility of the
suggested procedure.• Default parameters are sufficient for most cases.• The proposed procedure is capable of handling urban
areas with complex contents:– Tall buildings, low and nearby buildings, trees, bushes, fences,
bridges, ramps, cliffs, tunnels, etc.• Future work will focus on further testing of the proposed
methodology as well as improving its efficiency.• Also, the classified non-ground points will be further
classified into vegetation and man-made structures.– Building detection and change detection
![Page 83: DPRG Chapters 1 – 7: Overview · • LiDAR data includes ground/terrain and non-ground/off-terrain points. – Knowledge of the terrain is useful for deriving contour lines, road](https://reader033.vdocument.in/reader033/viewer/2022050516/5f9ff143740ff301d5543e1d/html5/thumbnails/83.jpg)
Ayman F. Habib83
DPRG
Laser Scanning
Comments and Questions?