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Canopy Segmentation using Airborne LiDAR

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Ashley Miller

on 19 May 2014

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Transcript of Canopy Segmentation using Airborne LiDAR

Canopy Segmentation using Airborne LiDAR
Outline
Data Overview
Goals
Primary Segmentation Methods
Application to DHM
Application to RGB Imagery
Edge Detection and Filtering
Application to DHM
Application to RGB Imagery
Findings and Statistics
Conclusions
Future Work
Data Overview
Hemlock Forest
Ground truth data for 43 trees collected during the 2012 Share Collect
Less dense comparatively
Only about 45min away
Basic Segmentation Methods
Watershed Method
Application to WASP RGB Imagery
THANK YOU!
Data Display
Rasterization by 1 meter bins
Digital elevation model
Digital surface model
Digital height model
WASP visible imagery
Digital Surface Model
Digital Elevation Model
Goals
Segment airborne LiDAR data into individual trees
Evaluate basic segmentation methods and compare results
Provide suggestions for future work based on findings
XY Euclidean Distance
XYZ Euclidean Distance
Watershed Method
Invert height model
"Fill" inverted model
Change filter sizes based on image size
XY Euclidean Distance
Find maximum points
Calculate euclidean distance to max points using X and Y
Find minimum euclidean distances
XYZ Euclidean Distance
Find maximum points
Calculate euclidean distance to max points using X, Y, and Z
Find minimum euclidean distance
Application to DHM
XY Euclidean Distance
XYZ Euclidean Distance
Watershed
Watershed
XY Euclidean Distance
XYZ Euclidean Distance
Red Channel Only
Green Channel Only
Blue Channel Only
Watershed
XY Euclidean Distance
XYZ Euclidean Distance
Watershed
XY Euclidean Distance
XYZ Euclidean Distance
Watershed
XY Euclidean Distance
XYZ Euclidean Distance
Edge Detection Methods Applied to RGB Imagery
Matlab Sobel Filter
Envi Sobel Filter
Edge Detection on Red Channel
Edge Detection on Green Channel
Edge Detection on Blue Channel
Matlab Sobel Filter
Matlab Sobel Filter
Matlab Sobel Filter
Envi Sobel Filter
Envi Sobel Filter
Envi Sobel Filter
Edge Detection Applied to DHM
Matlab Sobel Filter
Envi Sobel Filter
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Basic Segmentation Results for RGB Imagery
Watershed
XY Euclidean Distance
XYZ Euclidean Distance
779 segments total
Over segments by a factor of 18
3358 segments total
Over segments by a factor of 78
3358 segments total
Over segments by a factor of 78
Basic Segmentation Results for Red Channel
Basic Segmentation Results for Blue Channel
Basic Segmentation Results for Green Channel
767 segments total
Over segments by a factor of 18
Watershed
XY Euclidean Distance
XYZ Euclidean Distance
2096 segments total
Over segments by a factor of 49
2096 segments total
Over segments by a factor of 49
Watershed
868 segments total
Over segments by a factor of 20
XY Euclidean Distance
XYZ Euclidean Distance
4097 segments total
Over segments by a factor of 94
4097 segments total
Over segments by a factor of 94
Watershed
837 segments total
Over segments by a factor of 20
XY Euclidean Distance
XYZ Euclidean Distance
2366 segments total
Over segments by a factor of 49
2366 segments total
Over segments by a factor of 49
Edge Detection Results for the DHM
Edge Detection Results for RGB Imagery
Matlab
In general, 3% of pixels fell in borders
4602 in red channel
4639 in green channel
5161 in blue channel
Under segmentation
75% fell between a light and dark region
20% fell between two light regions
5% fell between two dark regions
Conclusions from Basic Segmentation
1. Trees with similar heights get clustered together
2. Trees with similar colors get clustered together
3. Unique heights and colors separate individually
4. RGB Imagery tends to over segment while DHM under segments
Trees of Similar Height get Clustered Together
Trees cluster by age and species
Lack of conifer trees means lack of distinct height changes
Potential Solutions
Begin with a lesser dense forest to test methods
Visually insert breaks before segmenting
Make use of point cloud to add another dimension
Trees with Similar Colors get Clustered Together
Single species' tend to have similar colors across trees
Trees of similar age tend to have similar color
Color varies with height
Potential Solutions
Attempt to zoom in to clusters of similar colors to reveal smaller changes in color
Begin with a lesser dense forest for testing
Begin with a more obviously variant forest for testing
Unique Heights and Colors Segment Individually
Segmentation methods rely on distinct changes in numbers across a field
Visually easier to see as well
RGB Tends to Over Segment while DHM Tends to Under Segment
Changes in individual tree canopies - eg. leaf clusters, branches, etc...
Rasterized form of DHM limits data
Potential Solutions
Create an averaging filter for RGB imagery before filtering
Use smaller bin sizes when rasterizing DHM
Conclusions from Edge Detection
1. Easier to detect between low ground and high trees
2. Difficult to find edges within similar clusters

Easier to Detect Between Low Ground and High Trees
More noticeable value change
Sobel is known as the "derivative filter"
Potential Solutions
Use for separating ground from trees
Use as initial phase not as sole segmentation method
Difficult to find Edges Within Similar Clusters
Not enough change within clusters
Similar trees grow closer together
Potential Solutions
Zoom in closer for similar clusters
Use leaf-off data
Use edge detection as initial clustering only
Potential Future Work
Collect leaf-off data for Hemlock Forest
Create DHM with smaller bin size
Attempt segmentation using spectral data
Attempt segmentation by height level
Attempt segmentation within zoomed in clusters
Attempt segmentation on blurred RGB imagery
WASP visible imagery for site
Basic Segmentation Results for DHM

58 total clusters
4 trees segmented individually
3 clusters of 7+ trees
67 total clusters
8 segmented individually
Remaining trees clusters in groups 3 or under
67 total clusters
4 segmented individually
7 trees grouped in clusters of 3 or more

Watershed
XY Euclidean Distance
XYZ Euclidean Distance
Matlab
Envi
64 pixels, or 4%, marked as edges
Under segmentation
Edge Detection Results for RGB Imagery
Envi
When segmented via euclidean distance, the Sobel results gave:
6464 segments in the red channel
7652 segments in the green channel
6464 segments in the blue channel
When segmented via Watershed , the Sobel results gave:
980 segments in the red channel
1089 segments in the green channel
1042 segments in the blue channel
Segmented using Watershed - 36 segments
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all trees in bigger clusters are within 3m of each other in height
http://www.gisresources.com/confused-dem-dtm-dsm/
Ashley Miller
Committee Members: Jan van Aardt, Paul Romanczyk, Bob Kremens
Ashley Miller
Committee Members: Jan van Aardt, Paul Romanczyk, Bob Kremens
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