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

Ayman F. Habib1

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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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OCCLUSION-BASED PROCEDURE FOR TRUE ORTHOPHOTOGENERATION AND LIDAR DATA CLASSIFICATION

Chapter 8

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Overview• Introduction• Orthophoto generation

– Literature review– Procedure

• LiDAR data classification– Literature review– Procedure– Experimental results

• Concluding remarks

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True Orthophoto Generation

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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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Perspective Image

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Orthophoto

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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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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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Digital Surface Model

perspective center

imagery

AB C

D

Orthophoto


ghost image/double-mapped area

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Generated OrthophotoOriginal Imagery

Double-mapped areas

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Digital Image

PC

Indirect (backward) transformation

Orthophoto Generation & Visibility Analysis

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perspective center

imagery

0 01 1 120

AB C

D

a b c

DC P.C

longer

Invisible point


Orthophoto

Z-Buffer Method

True Orthophoto Process – Existing Method

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Generated True OrthophotoOriginal Imagery

Z-Buffer Method

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Generated True Orthophoto

Z-Buffer Method

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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


Orthophoto

Angle-based Method

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00

ii

Radial Sweep for the Angle-Based Method


Angle-based Method

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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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Conceptual procedural flow of the spiral sweep method

DSM

column

row

target point

nadir point

True Orthophoto Gen.: Spiral Sweep

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Comparative Analysis

Z-buffer method

Angle-based (spiral sweep) method

Differential rectification

Angle-based (adaptive radial sweep) method

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Original Image

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LiDAR Surface Model

Elevation Data Intensity Data

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Orthophoto with Ghost Images

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True Orthophoto without Ghost Images

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True Orthophoto After Occlusion Filling

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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


Orthophoto

Occlusion Extension

Angle-based Method

16°

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True Orthophoto After Occlusion Extension

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True Orthophoto After Boundary Enhancement

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Orthophoto with Ghost Images

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True Orthophoto without Ghost Images

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True Orthophoto After Occlusion Extension

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True Orthophoto After Boundary Enhancement

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Classification of LiDAR Data(Ground/Non-Ground Points)

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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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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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LiDAR Classification: Literature• Categories (Sithole & Vosselman 2003):

– Slope-based– Block-minimum– Surface-based– Clustering/segmentation

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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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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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LiDAR Classification: Processing Flow

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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

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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

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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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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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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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• Non-ground points detected from projection centers with different horizontal locations

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• Non-ground points detected from projection centers with different vertical locations


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• 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

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The eight neighbors of any given pixel are checked to see if they are occluded by that pixel or not.

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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


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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

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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


For Pixel A


For Pixel A


For Pixel APerspective

Center 8For Pixel A


For Pixel A


For Pixel A


For Pixel A

dd

d

d

45 Degree

B


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LiDAR Classification: Results

Simulated Dataset DSM Identified Occluding Points (in white)

Simulated DatasetMisclassified ground points

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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

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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

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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

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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

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LiDAR Classification: Filtering

• Examples of outliers: multi-path errors, errors in the laser range finder

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Ground Non-Ground Ground Non-Ground

GroundTARGETHeight

groundNonTARGETHeight


GroundTARGETHeight

GroundTARGETHeightKeep it


GroundTARGETHeight

OutlierTARGETHeight

Outlier

OutlierThreshold

HeightHeightFreq

uenc

yLiDAR Classification: Filtering

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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

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DSM

Classification Results using filterClassification Results without filter

Simulated Dataset

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Real Dataset (1 - Brazil)

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Occluding points in white

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After Statistical Filtering

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DSM → Non-ground objects


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• 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%.


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Misclassified Points Misclassified Points displayed on DSM


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Original DSM Derived DTM

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DSM Occluding Points Non-ground Points

Discontinuous Terrain: Tunnels

Real Dataset (2 - Stuttgart)

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DSM

Occluding Points

Non-ground Points


Real Dataset (2 - Stuttgart)

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DSM Occluding Points Non-ground PointsReal Dataset (2 - Stuttgart)

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• 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)

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Non-ground points (TerraScan) Non-ground points (Occlusion-based)


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TerraScan’s Result

Occlusion-Based Result


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• Another ROI near the University station is selected as another experimental data.

• Complex contents – The Transit Train station,– Bridge,– Ramps, and– Trees.


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Non-ground points (TerraScan) Non-ground points (Occlusion-Based Results)



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TerraScan’s Result

Occlusion-Based Result


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Original LiDAR Points over UofC



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Aerial Photo over UofC



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Ground/Non-Ground Points



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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

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Comments and Questions?

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

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