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Section 10: Lidar Point ClassificationSection 10: Lidar Point Classification
Outline
Example from One Commercial Data Classification Software Package
University of Texas at Austin Center for Space Research (CSR) Data Classification Software Examples
• Copan Ruinas, Honduras• Austin, Texas• Matagorda Island, Texas Gulf Coast
Commercially Available Data Classification Software
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wal_allpo ints
Unable to distinguish between buildings and vegetation
Copan Ruinas, Honduras
Archaeological Park includes Mayan ruins, open park-like areas, and dense tree cover
Above: A significant amount of the LIDAR energy can penetrate the forest canopy just like sunlight
UT CSR Data Classification
Can distinguish between ground, vegetation, and buildings
Copan Ruinas, Honduras all points DEM buildings and ground DEM Havard total station survey
UT CSR Data Classification Research
UT data classification algorithms are useful in constructing a “bald earth” topography, estimating vegetation heights, and identifying buildings and other artificial structures
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Above: Lower Envelope Follower defining the lower surface of a simple, AM signal.
Lidar signals are separated into high and low frequency components. A Lower Envelope Follower is used to identify ground surface in the high-frequency LIDAR signal. Envelope follower circuits are commonly used to demodulate amplitude-modulated (AM) signals [Weed and Crawford, 2001]
Simplified Terrain and Building Extraction Methodology
Data are filtered to remove anomalous (long/short) ranges A 1m1m DEM is constructed from the minimum elevation values in each grid Average surface calculated from minimum grid using a square-average filter Average surface subtracted from the minimum grid to create a high-pass, 3-D signal with a
zero-mean Lower-envelope follower (LEF) algorithm approximates the ground surface in the high-pass,
3-D signal High-pass signal is thresholded with the lower-envelope signal to create a ground mask
A gradient flood-fill procedure used to fill holes in the ground-mask signal
Buildings are detected by their planar roofs and are removed from the ground-mask
Data are classified as buildings or ground reflections using the final ground-mask
Vegetation Extraction Methodology
System Flow Chart for Bare Earth Terrain Extraction from First and Second Return Data
Vegetation heights are estimated by creating a maximum surface using the first-return data
Similarly to the minimum surface, the first-return LIDAR data are edited for short and long ranges and then gridded into a 1m 1m array
The maximum grid is smoothed using a square-average filter An average surface is subtracted from the maximum grid and the LEF is used to
define the ground surface in the high pass signal The high-pass signal is thresholded with the lower-envelope signal to create a
vegetation mask. The thresholding is set above the previously detected ground and building surfaces
Classification of Austin Data
Austin data used to develop CSR classification algorithms.
These panels show the bald earth topography, the building
masks, and the vegetation (tree) heights derived from LIDAR data of the northeast
corner of the University campus. All elevations are
HAE in meters
Building detection masks constructed using first and last return data. Masks define the surface of artificial structures and are used to classify data as being reflections from buildings or bridges.IKONOS image of University campus showing a
mix of buildings, tree-lined creeks, and open areas
Bald-earth topography defined by Lower Envelope Follower and interpolated from last return data
Vegetation heights derived from minimum and maximum grids. Heights are meters above ground
Data Classification
Filtering algorithms distinguish laser reflections from the ground, trees (green), and buildings (red). These filters are used to construct “bald earth” topography, estimate vegetation heights, and identify buildings and other artificial structures.
Data Classification Limitations
Above: Matagorda Island Gulf of Mexico shoreline– all points, color-coded elevation image of 1-meter gridded data
Below: Matagorda Island – difference between all points and bare earth grids of area shown to left
Difficult to classify lidar data in areas with low topographic relief and dense vegetation
Low Topographic Relief
Above: Matagorda Island Wynn Ranch runway – all points, color-coded elevation image of 1-meter gridded data
Below: Matagorda Island difference between all points and bare earth grids of area shown to left
Difficult to classify lidar data in areas with low topographic relief and dense vegetation
Waveform Digitization
Track of ICESat over Silver Island MountainsSimulated GLAS waveforms along the 183-
day ICESat track
The key to data classification in low relief, densely vegetated coastal environments will be wave-form digitization