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Section 10: Lidar Point Classification

Section 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.

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Section 10: Lidar Point Classification

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  1. Section 10: Lidar Point Classification

  2. 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

  3. Commercially Available Data Classification Software • Unable to distinguish between buildings and vegetation

  4. 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

  5. UT CSR Data Classification Copan Ruinas, Honduras all points DEM buildings and ground DEM Havard total station survey • Can distinguish between ground, vegetation, and buildings

  6. 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 • 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] Above: Lower Envelope Follower defining the lower surface of a simple, AM signal.

  7. Simplified Terrain and Building Extraction Methodology • Data are filtered to remove anomalous (long/short) ranges • A 1m1m 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

  8. Vegetation Extraction Methodology • 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 System Flow Chart for Bare Earth Terrain Extraction from First and Second Return Data

  9. 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

  10. 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.

  11. Data Classification Limitations • Difficult to classify lidar data in areas with low topographic relief and dense vegetation Below: Matagorda Island – difference between all points and bare earth grids of area shown to left Above: Matagorda Island Gulf of Mexico shoreline– all points, color-coded elevation image of 1-meter gridded data

  12. Low Topographic Relief • Difficult to classify lidar data in areas with low topographic relief and dense vegetation 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

  13. Waveform Digitization • Track of ICESat over Silver Island Mountains • The key to data classification in low relief, densely vegetated coastal environments will be wave-form digitization Simulated GLAS waveforms along the 183-day ICESat track

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