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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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