William Barragán Zaque

Ingeniero Catastral y Geodesta

Especialista en Sistemas de Información Geografica

Msc. Photogrammetry and Geoinformatics

Director Tecnología en Cartografia

Universidad de Cundinamarca

wbarragan@unicundi.edu.co

Abd-el-Hamed Nabial Ibrahim

NATIONAL AUTHORITY FOR REMOTE SENSING

AND SPACE SCIENCES (NARRS), cairo

msc Education- nttti chenai - India

Msc. Photogrammetry and Geoinformatics

Nabila_gis@yahoo.com

Airborne Laser Scanning

Resumen Este artículo presenta algunas características y aplicaciones del sistema de escaneo láser. El Airborne Laser Scanning es un sistema relativamente nuevo que permite la generación de cartografía utilizando escaneo del terreno, donde es posible diferenciar el modelo digital de superficie del modelo digital del terreno. Se presenta adicionalmente el funcionamiento general del sistema, dando sus principales ventajas en la extracción automática de edificaciones.

KEY WORDS: Laser Scanning, Reconstruction, Level-of-Detail, LiDAR, Image processing.

Introduction

Light Detection And Ranging (LIDAR), laser scanning, and laser

altimetry are terms used to describe technology of reconstructing earth

surface or objects on the ground using laser beams from the air. Laser

scanning is widely used in terrestrial and airborne applications to

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reconstruct surfaces with a high level of detail. Since the basic compo-

nents of airborne laser scanning and working principles of airborne laser

scanning are well discussed in many literatures,1

Laser Scanning

Light amplification through stimulated emissions of radiation is known as

LASER. Laser beam is used to determine the range between source and

object.

When the purpose of laser ranging is to reconstruct an object

surface, laser beam can be scanned on the object. This group of

ranging points is called point cloud and this scanning technique is called

laser scanning

If the beam emitting device is fixed on the earth to reconstruct an

object, it is called terrestrial laser scanning.

If the laser device is fixed on an airborne vehicle and earth surface

is scanned, it is called airborne laser scanning.

The main advantage of ALS data is that it does not depend on weather

conditions, therefore output is highly accurate compared to traditional

photogrammetry.

1 Schenk T., 2004, Airborne Laser Scanning, Lecture notes (unpublished), Stuttgart University of Applied sciences

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Figure 1: Point Cloud

Measuring Principle

Laser has been used in geospatial sciences for several years. Laser

equipment basically consists on an emitting diode that produces a light

source at a very specific frequency. The signal is sent toward earth

where it is reflected back towards the sensor platform. Then, a receiver

device captures the returning pulse signal. By measuring the time lapse

between the sent and received signals, the distance to surface can

be measured.

Pulses and Returns

Laser pulses transmitted towards earth are reflected, absorbed and

scattered based upon the surface characteristics. Reflected pulses are

received by a receiver device in ALS system. While pulses pass through

vegetation, some particles of the laser beam are reflected back from

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D = v. t / 2

VA=D cos αA

Where

c – velocity of light

t – Time lapse

VA-Elevation of point

αA -angle from the nadir

Figure 2: Measuring principle

the branches of tree, while the rest of them are reflected from earth

surface. The reflections coming from the tree branches reach the

receiver first and the reflections from the ground reach it last. These first

and last reflections are called first and last pulses respectively. There are

many possible reflections, but in research only first and last pulses are usually

used. Due multi-reflectivity nature of laser beams, different forestry

applications have been possible.

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Classification of ALS data:

Raw ALS data point cloud is both dense and noisy, therefore

noises in the ALS data have to be filtered out, there are several

methods implemented based upon the nature of noises.

Classification of ALS is the next step in ALS data processing.

Classification is the process of segmenting the point cloud into

different classes like ground, vegetation, building etc.

ALS data is normally processed as a point cloud data. In certain

applications, ALS data is rasterized using elevation as intensity

values. Rasterized ALS data is called ALS image or LIDAR image.

Sharp break line information can be retrieved only from the

vector data such as point cloud. The following is a general

classification implemented in ALS data.

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Figure 3: First pulse and last pulse

Ground point classification:

Ground points are the ALS points lying on the bare earth surface.

These points create a digital terrain model (DTM). There have

been many techniques invented by re- searchers to perform ALS

data ground classification automatically. This process of segregating

ground points from point cloud can be achieved by local slope of

the terrain or statistical methods. Physical objects like buildings,

towers, vehicles need to be removed. Also, points lying on

vegetation are filtered out. Due to the density and accuracy of ALS

data high accuracy contours like 1 foot, 2 feet, 5 feet can be

generated.2

Vegetation classification

Vegetation classification is a process of segregating vegetation

points from the point cloud. The vegetation boundary can also

be extracted from the classified vegetation points. Multiple returns

from laser pulses enable the researchers to classify the vegetation

points easily.

Laser scanning has vast potential for the direct measurement and

2 Schenk T., Digital Photogrammetry, TerraScience, Laurelville, OH 2001

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Figure 4: Classification (a) Raw ALS data (b) Ground points ALS data

Figure 5: Raw ALS triangulated points

estimation of several key forest characteristics. The direct

measurements can be canopy height, sub canopy topography, and

the vertical distribution of intercepted surfaces between the

canopy top and the ground. Other forest structural

characteristics such as aboveground bio- mass can also be

modeled or inferred from these direct measurements.

Building points classification:

Digital surface model (DSM) from the point cloud allows

extracting the features like buildings, roads, and other physical

objects automatically. Building planes can be detected using

mathematical characteristics like surface normal, curvature etc.

Extraction of planar building planes and curved planes are possible

from the point cloud. But the detection of building outline has been

a difficult task unless the building footprint is provided.

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Miscellaneous object classification

Other than ground, vegetation and buildings features like

transmission lines, roadways can also be extracted. Especially with

the high density ALS data power lines can be modelled easily.

Potentials for 3D building extraction

ALS technology clearly shows its potential in building extraction, the

following are con- sidered as the potentials for extracting buildings

from the ALS data.

Computational geometrical algorithms can be adopted to

detect the shapes of the building roof, walls and other

prominent structures.

High ALS point density is another factor helps to reconstruct

the building mod- els accurately.

Vertical accuracy can be reached to 5cm in ALS.DSM can

also be extracted from the aerial images using image matching

techniques, but 5 cm or less accu- racy is highly difficult in digital

photogrammetry.

ALS is noise free when compared to the aerial images for building extraction.

If the ALS is done in low altitude, building walls are likely to be

hit by the laser pulses. This will allow reconstructing the walls of

buildings.

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References

Alharthy Abdullatif and Bethel James S. (2002). Heuristic filtering and 3D feature extraction from lidar data. In ISPRS Commission III, Symposium 2002 September9 - 13, Graz, Austria.

KRAUS, L. Photogrammetry. Vol. I: Fundamental and Standard Processes. 4ª Edición. Vol. II: Advanced Methods and Applications. 4ª Edición. Ed. Dümmler, Colonia, Alemania. 1992, 1997. 397 p. (vol. I), 466 p. (vol. II).

Schenk T., 2004, Airborne Laser Scanning, Lecture notes (unpublished), StuttgartUniversity of Applied sciences.

Schenk T.,(1999)Digital Photogrammetry, TerraScience, Laurelville, OH 2001

Sohn G and Dowman, I. (2003). Building Extraction Using LIDAR DEMs and IKONOS Images, ISPRS proceedings, Volume XXXIV, PART 3/W13. Dresden, Germany,8-10 October.

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