Page 18

Model

Manufacturer

Lidar unit

Camera unit

Laser wavelength PRF(*) [kHz] [nm]

FOV [°]

Scan pattern

Vertical accuracy [cm](**)

# cameras

Spectral bands

Image resolution [MPx]

Focal length [mm]

Hexagon/ Leica

1064

700

40

circular

<5

1 nadir + 4 oblique

RGB + NIR (nadir), RGB (oblique)

5 x 80

80 (nadir), 150 (oblique)

TerrainMapper Hexagon/ Leica

1064

2000

20-40

circular

<5

1

RGB+NIR

80

50, 80

SPL100

Hexagon/ Leica

532

6000

20-30-60

circular

< 10

1

RGB+NIR

80

50, 80, 150

Chiroptera 4X

Hexagon/ Leica

515/1064

500/140

28/40

elliptical

< 5 (topo)

1

RGB+NIR

80

50

HawkEye 4X

Hexagon/ Leica

515/1064

500/ 140/40

28/40

elliptical

< 5 (topo)

1

RGB+NIR

80

50

VQ-1560i-DW

RIEGL

532/1064

2 x 1000

58

cross lines btw 2 channels

2

RGB+NIR

Up to 150

35, 50, 80

Up to 150

CityMapper

CP-780

RIEGL

1064

1000

60

parallel lines

VQ-880-GII

RIEGL

532/1064

700 /900

40

circular/ 2.5 curved parallel lines

2

2

RGB+NIR

Up to 2

RGB and/or IR 100

VQ-840-G

RIEGL

532

100

40

elliptic

Galaxy T2000

Teledyne Optech

1064

2000

10-60

sawtooth

Eclipse

Teledyne Optech

1550

450

60

parallel lines

Titan

Teledyne Optech

532/1064/1550

900 (total)

60

lines

CZMIL Nova

Teledyne Optech

532/1064

10-80

40

LiteMapper -7800VQ

IGI

1064

1000

LiteMapper4800VQ

IGI

1550

LiteMapper -1560VQ

IGI

LiteMapper -5800VQ

IGI

1.5

1

RGB

12

50

<5

1-4

RGB + NIR

150

50, 70

<5

1

RGB

30

35

<5

Up to two

RGB/NIR

29/80

-

circular

15 (2σ)

2

RGB/ hyperspectr.

100

-

60

parallel lines

2

Up to 5

RGB/NIR

150

40, 50

2000

75

parallel lines

2

Up to 5

RGB/NIR

150

32, 40, 50

1064

2x1000

60

cross lines b/w 2 channels

Up to 2

RGB/NIR

150

40, 50

1064

2000

75

parallel lines

Up to 5

RGB/NIR

150

32, 40, 50

2

35, 50, 80 50

Table 1: Overview of the most recent airborne hybrid systems available on the market. (*)Max. pulse repetition frequency; (**)1σ value, under conditions specified in the instrument datasheets.

of both measurement techniques is a fundamental prerequisite. Secondly, many Lidar-related, camera-related and trajectoryrelated parameters are involved within the integrated adjustment, and it is not easy to define their respective role (as unknowns, soft constraints or hard constraints) and weight. A rigorous hybrid adjustment is, for instance, implemented in the OPALS software by TU Wien. 3D reconstruction based on dense image matching, as facilitated by the SURE software by nFrames, is already a standard in the industry to produce dense 3D point clouds, digital surface models, true orthophotos and 3D meshes. It particularly benefits from the high availability of aerial image data, low acquisition costs, fine resolution of detail and availability of high-resolution multispectral information. If additional Lidar data is available and well co-registered, these results can be improved by the complementary sensor behaviour. Dense surface generation 18 |

from imagery is particularly strong on detail and edges due to the high resolution defined by the pixel ground resolution. Meanwhile, Lidar technology is strong in its ability to retrieve low noise samples consistently with homogenous precision due to the reliable depth measurement of the active laser beam. This is particularly beneficial in the presence of poor texture, such as strong shadows or large white surfaces, where the passive texture matching is limited by the ability of the camera to resolve texture. Here, Lidar data can support the surface generation by additional depth measurements for better precision and completeness. Furthermore, polar measurements are helpful in case of small yards and very narrow streets, where the laser beam can occasionally reach the ground, while DIM reconstruction is often prevented by stereo-occlusions. Lastly, forestry applications additionally benefit from multiple returns and full waveform information of Lidar data. By integrating both data sources, this high completeness and

reliability can support the high-resolution result from DIM, which delivers high fidelity along edges and other discontinuities, fine surface detail and particularly multispectral colour information.

Integration challenge The main challenge in integrating Lidar and DIM data consistently lies in proper consideration of their high variations in resolution and precision. In aerial applications, the dense image matching point cloud is typically of higher density and lower depth precision than the Lidar data when captured at high altitude, e.g. from fixed-wing aircraft. This is due to the resolution limitation of the Lidar beam divergence and repetition time on the one hand, and the availability of high-resolution large-frame cameras on the other. Furthermore, the variations of the local point-cloud precision for dense image matching can be high, particularly as the point precision depends not only on the ground resolution but also on the texture

international | s eptem ber/october 2019

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Profile for Geomares Publishing

Gim international september october 2019  

https://www.gim-international.com/magazines/gim-international-september-october-2019.pdf

Gim international september october 2019  

https://www.gim-international.com/magazines/gim-international-september-october-2019.pdf