Two Approaches for Dense DSM Generation from Aerial Digital

Oblique Camera System

Massimiliano Pepe and Giuseppina Prezioso

Department of Sciences and Technologies, University of Naples “Parthenope”, Centro Direzionale Isola C4, Naples, Italy

Keywords: Oblique Camera System, DSM, Photogrammetry, GPU, SfM, Aerial Survey.

Abstract: In recent years, in photogrammetric field, have been developed technologies, which consist of multi digital

oblique camera, able not only to observe the same target from different angles, but also to determine, thanks

to appropriate dedicated software, the geometry. Of particular interest is the new oblique camera system

Leica RCD30 that combines vertical (nadir) and oblique cameras according to the “Maltese cross”

characteristic scheme. The purpose of this work is to verify the potential of the oblique imagery to provide

dense point clouds to realize Digital Surface Model (DSM) to high resolution, where for high-resolution

model is meant a representation of the observed scene with a ground sample distance (GSD) of less than

10cm. The dense Digital Surface Models are obtained through two different approaches, one that derived

from photogrammetric reconstruction based on graphic processing units (GPU) technique and multi-core

CPUs, the other from so-called Structure from Motion (SfM). To analyse the quality both of acquisition

systems that the model surface obtained from images, a case study on the Nöllen (Switzerland) area is

presented.

1 INTRODUCTION

The oblique camera system is designed for high

accuracy 3D city models (Zhang et al., 2004, Wang

et al., 2008), cadastral applications (Pepe et al.,

2012), cultural heritage field (Nocerino et al., 2013)

and for the realization of DSM (Le Besnerais, 2008;

Madani, 2012; Cavegn, 2014).

Especially for the production of DSM, the recent

development of dense image matching methods, by

deriving point clouds from imagery, provides an

efficient alternative at airborne LiDAR systems

(Fritsch et al., 2012). In fact, the first experiments

the 3D point clouds concerning by oblique camera

have shown the potential using sample image

processing algorithm and the very dense point

clouds can be filtered to obtain even digital terrain

models (Fritsch et al., 2013).

The actual oblique camera system comes in a

variety of configurations, which differ in the sensors

number, format, arrangement, mode of acquisition

and spectral sensitivity (Rupnik et al., 2014).

The oblique camera system taken into

consideration in this paper is the Leica RCD30

Oblique Penta System, which consists of five

cameras: four cameras inclined at 35° and arranged

according to an orientation north, south, west, east

and one nadir camera.

The shape of the ground coverage captured

simultaneously by the five cameras looks like a

“Maltese Cross” (Figure 1), a term first coined by

Gordon Petrie and Kenneth Smillie (Petrie, 2009).

The several cameras are mounted rigidly together

and their geometric configuration is calibrated to

enable accurate measurements in both the vertical

and oblique images.

Figure 1: Leica RCD30 Oblique Penta footprint with

RCD30 cameras.

The Leica RCD30 medium format camera is the

first 60 Mp camera able to acquire co-registrated

multispectral RGB and NIR imagery from one

Pepe, M. and Prezioso, G.

Two Approaches for Dense DSM Generation from Aerial Digital Oblique Camera System.

In Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2016), pages 63-70

ISBN: 978-989-758-188-5

camera head (Wagner R., 2011). The features of

camera are reported in Table 1 (Leica Geosystems,

2015).

Table 1: Features of Leica RCD30 Oblique Penta.

CCD Size

8956

6708

ixels

Pixel Size

namic Ran

e of CCD

73 dB

Resolution A/D Converter

14 bit

Maximum Frame Rate

(

Penta

)

1.8

Motion Com

ensation

Mechanical, bi-directional

ectral Ran

Core

istere

Furthermore, this sensor connected with

GNSS/INS systems allows to accelerate the

workflow for the construction of elevation models.

In fact, the direct georeferencing (DG) system

correlates directly the data collected by a remote

sensing system to the Earth, by accurately measuring

the geographic position and orientation of the sensor

without the use of the traditional ground-based

measurements (Mostafa et al., 2001; Cramer, 2010;

Pepe et al., 2015a).

In addition to the acquisition system is necessary

define the appropriate processing technique of

image. The following section describes two

approaches.

The first coming from the computer vision

community and known as Structure From Motion

(SfM) algorithms while the second approach is

based on use of the GPU technology in combination

with multi-core CPUs which produces unmatched

processing speeds for the creation of geospatial data.

In the two approaches the quality of the DSM

depends by image of the photogram or better, within

the period of digital photogrammetry, by the Ground

Sampling Distance (GSD).

2 GSD, SCALE NUMBER AND

ACCURACY OF THE OBLIQUE

IMAGES

For each digital camera, the combination of the focal

length and pixel size determines a specific Ground

Sampling Distance (GSD).

In the case of vertical aerial flight, the

relationship between the GSD to the relative flight

altitude is (Neumann, 2008):









(1)

where:

GSD Ground Sampling Distance;

f Focal length;

S Pixel size;

H Relative flight altitude.

Considering the Leica RCD30 camera, using a focal

length of 50 mm and a pixel size of 6 µm, the

relation between the flight altitude and the GSD, in

the case of vertical aerial flight, is reported in Table 2.

Table 2: GSD at different flight altitude in vertical aerial

survey.

GSD

(cm)

Image

scale

Height

above

ground

(m)

Footprint

along track

(m)

Footprint

cross track

(m)

3 1:5000 250 20125 26870

5 1:8333 417 33542 44783

8 1:13333 667 53667 71653

10 1:16667 833 67083 89657

15 1:25000 1250 100625 134350

20 1:33333 1667 134167 179133

30 1:50000 2500 201250 268700

Figure 2: Parameters of oblique images (Hohle, 2008).

The scale within an oblique image (Figure 2)

depends of several parameters (Hohle, 2008), as

shown below:



cos



cos

(2)

GISTAM 2016 - 2nd International Conference on Geographical Information Systems Theory, Applications and Management

where:

m Scale at target point;

t Tilt angle;

β Angle between the viewing ray to a

target and the vertical;

 Half of field of view.

The scale, m

PP’

, at principal point of image, PP’, is:













cos

(3)

By fixing the flight altitude (derived from Table 2

for each GSD) and choosing the parameters of the

photogrammetric system under consideration, the

scale at principal point of an image oblique assumes

the values given in Table 3.

Table 3: Scale at the principal point using the RCD30

system parameters.

Relative flight altitude (m) Image scale GSD (cm)

250 1:6104 4

417 1:10181 6

667 1:16285 10

833 1:20338 12

1250 1:30519 18

1667 1:40701 24

2500 1:61039 37

This means that at the same flight altitude occur

several GSD according to the cameras scheme and,

as can be observe from Tables 2 and 3, with

increasing altitude, the difference in GSD between

vertical and oblique aerial flight becomes more

important.

For example considering the flight altitude of

2500 m, if the flight of acquisition is vertical we get

a GSD = 30 cm, whereas if we choose the oblique

aerial flight we get a GSD = 37 cm.

Therefore, the GSD varies depending on the

geometry of the photogrammetric aerial oblique

flight and this is to be taken into account in the

planning phase of flight to obtain a specific

accuracy.

In the study the accuracy of oblique stereo image

occurs two cases: tilt across track (side-looking) and

tilt along track (forward-looking), whose geometry

in oblique stereo image is shown in figure 3 (Gerke,

2009).

Figure 3: Several geometry in oblique stereo image –

looking across track (up) and along track (down).

In the first case (tilt across track) the accuracy

value (Gerke, 2009) is:











≅











cos







,



sin





,

≅











sin







,



cos



(4)

where:







≅













(5)



,



≅







(6)

and indicating with:









parallax accuracy

′

(7)

In the second case (tilt along track) the accuracy

(Gerke, 2009) is:





















≅









≅

































sin



















,



≅















(8)

Two Approaches for Dense DSM Generation from Aerial Digital Oblique Camera System

where:

parallax which needs to be computed for the

estimation.





≅

(9)

∆



≅sin

(10)







≅



(11)







≅





∆′

(12)

3 CASE STUDY

The nadir and oblique images are in Tiff format and

have been acquired on the Nöllen area, Widnau,

Switzerland (Figure 4).

The complete block consists of 625 images at 5

cm GSD and 300 images at 8 cm GSD with 30%

side overlap and 60% forward overlap. The external

orientation of every image captured, given in UTM

Zone 32 North, is calculated using the software

IPASCO and the Leica Photogrammetry Suite. The

misalignments and principal points of auto-

collimation (PPA), for the five different cameras,

have not been corrected in this original dataset. Of

this photogrammetric block has been selected a

limited area of particular interest and were chosen the

frames of the flight plan with GSD equal to 5cm.

Figure 4: A snapshot from Google Earth

showing the

location of the study area.

The file size of each aerial photo is about 487

MB. The processing was carried out using a PC with

the following configuration: Intel® Core™ 2.50

GHz CPU, 8 GB RAM.

3.1 DSM by using Structure from

Motion (SfM) Photogrammetry

Many tools designed for SfM applications collapse

due to the enormous number and size of the images

used in mapping projects. An idea to overcome this

limitation is to exctract from the block a

photogrammetric sub-block. In particular, have been

used 12 pictures of the block having a GSD of 5 cm

in order to obtain a higher resolution of the DSM

and orthophoto. The images have been chosen to

allow a stereoscopic coverage both in the sense of

the flight that transversely.

In this section, Agisoft Photoscan Pro software

(Agisoft, 2014) was used for 3D data processing.

This software works mainly on computer vision

based techniques. Structure from Motion (SfM), and

multi-view reconstruction techniques are the main

principles on which Agisoft creates the 3D model of

an object. In this software, both image alignment

and 3D model reconstruction are fully automated

(Sing, 2014). The 3D model reconstruction is

obtained in several straightforward processing steps.

Firstly Agisoft allows, through an algorithm

called “feature detection algorithm” by identify,

automatically, features in overlapping pictures and

match them. When the process is repeated for all

feature points in the dataset, the result is a sparse

point cloud which is a 3D approximation of the

scene in the pictures (Semyonov, 2011). The

alignment operation of the 12 images was performed

in 435 seconds. At this point, since both the original

camera positions and the cameras calibration are

known, the software can build a dense points cloud

(Van Damme, 2015) using a more computationally-

intensive algorithm. The Agisoft software offers

different options for to construct the dense points

clouds (Low, Medium, High, Very High), among

them the authors have chosen the “High”, because the

computer was not able to process in “Very High” mode.

Subsequently, in the “Build Mesh” step,

PhotoScan creates a surface mesh from the dense

point cloud. A representation of the geometry of the

images and the DSM generated in Agisoft

environment, is shown in Figure 5.

The software enable the export the point clouds

in several format; the LAS ASPRS file format allows

quickly to read and interpret the elevation ASCII data

and at the same time reduces the file size. However,

GNSS systems have led to the use of the ellipsoidal

height and, consequently, also the point clouds are

obtained in the ellipsoidal reference system. To

transform the ellipsoidal in ortometric height is

necessary to know a geoid undulation model. The

geoid undulations was taken from the geoid model

EGM2008 (Pavlis et al., 2008) and, with using a

software developed in Matlab® by the authors, has

been possible to transform the point clouds from

GISTAM 2016 - 2nd International Conference on Geographical Information Systems Theory, Applications and Management

ellipsoidal to orthometric height (Pepe et al., 2015b).

Figure 5: Camera positions and elevation model.

Subsequently the point clouds obtained were

converted into raster format GeoTiff with grid

10cmx10cm (Figure 6).

Figure 6: DSM of the study area obtained with Agisoft

Photoscan Pro.

3.2 DSM Obtained using GPU Method

In recent years is becoming more common, in

photogrammetric applications, the use of parallel

programming and computing power of the graphics

processing unit. In fact the modern graphic

processing units are not only powerful graphic

engines but also they are high level parallel

programmable processors with very fast computing

capabilities and high-memory bandwidth speed

compared to central processing units (CPU) (Sahin,

2012).

A commercial software that uses this approach is

Correlator3D ™ with which it is possible to realize a

DSM in two main phases: the aerial triangulation

and, subsequently, the generation of the model.

The first task for the production of the DSM is

the “Aerial triangulation”, which begins with the

extraction of the tie points. Correlator3D™

calculates the tie points by identifying, in adjacent

images, some feature points common and once done

that, these feature points are matched within each

flight line between adjacent images. The final step in

aerial triangulation is the “Bundle adjustment”. In

this process is performed, on all the images, a

minimization routine which determines, in iterative

mode, a unique correction for the exterior

orientation (EO) parameters in such a way as to

reduce the average residual between the tie points

and the projected points (Simactive, 2015). For

DSM generation is used the graphics processing unit

where the images are loaded into the GPU the

memory on a pair-by-pair basis, significantly

reducing the memory constraints on the system. The

process begins by loading a pair of images into the

GPU memory and a DSM patch, corresponding to

such pair, is created and stored on disk. This process

repeats until all the images have been processed. The

resulting overlapping DSM patches are then

optimized and merged in the following manner.

Within every DSM patch, to each point is associated

a weight based on a confidence measure. This

measure depends on different metrics including one

that assigns a weight to the elevation values as a

function of their distance from the center of the

DSM patches, this to reduce the potential occlusion

problems (Rotenberg, 2013). In the phase of the

Aerial Triangulation the average tie point residual

error was of 0.36 pixel and the required processing

time for obtain the DSM has been of some minutes.

Figure 7: DSM of the study area, by Correlator3D™

software, and profile elevation (ellipsoidal height).

The DSM realized by Correlator3D™, and

represented in Global Mapper environment (Figure 7),

shows the quality with which the software can

Two Approaches for Dense DSM Generation from Aerial Digital Oblique Camera System

describe the territory. In fact, from the elevation

profile it can be seen not only the typical shape of

the roofs and of the shape of the buildings, but also

the shape of the street gutter. Subsequently, in order

to check if the software shows limitations in the

treatment of numerous images, has been analysed a

block photogrammetric much wider of that

previously used. From this test it was found that the

software has been able to process 625 images, in

4725 sec. for the aerial triangulation processing and

81000 seconds for the production of the DSM.

3.3 DSM Comparison

The two DSM were compared with each other both

in terms of quantity and quality. From the

quantitative point of view means to compare the two

models both planimetrically that altimetrically. The

results obtained using some of the benchmark

showed a shift planimetric and altimetric contained

in the order of a few centimetres. The qualitative

comparison aims to analyse the ability, of the DSM,

to represent spatial objects (houses, dormers, trees,

etc.). The DSM, which has been produced, with

Correlator3D™ has allowed a (little) better

description of spatial objects than to Agisoft

software (Figure 8).

Figure 8: DSM comparison - Correlator3D™ (left) and

Agisoft (right).

Nevertheless qualitative and quantitative aspects

are interrelated. For example if the model obtained

with Agisoft is not able to represent a tree or a fence

means that this DSM, compared with that generated

by Correlator3D™, will get of the height differences

also important. In order to quantify the difference

between the two DSM, obtained with different

approachs, it has been employed the QuantumGIS

software, using the mathematical operator raster

“difference”.

From the comparison of the raster maps (Figure

9) can be deducted the following considerations:

 the vast majority of the territory has a difference

contained in the value of 5 cm, demonstrating the

accuracy that can be achieved with both software

tested;

 the only elements that showing notable

differences in height are the trees;

 few and limited vertical shift occurred along the

edges of some buildings with the ability to

Correlator3D™ to produce a point cloud denser

and therefore represent in greater detail some

spatial objects; this means that a more dense

point clouds allows you to distinguish some

structures of the building, such as gutters,

chimneys and protruding elements.

Figure 9: Difference Map (meters) between the two

models (up) and orthophoto (down).

As regards processing times, the point clouds

generated by the software Correlator3D™ are

obtained in a time less than Agisoft. In addition the

first software showed a greater capacity to handle a

large number of images and, consequently, to

represent a wider area of the territory.

4 CONSIDERATION ON AERIAL

SURVEYS WITH OBLIQUE

CAMERAS SYSTEM

The traditional photogrammetric workflow overlap

(60% forward and 30% side overlap) creates

occlusion areas and reduces the redundancy of

GISTAM 2016 - 2nd International Conference on Geographical Information Systems Theory, Applications and Management

image information. Therefore it is desirable in the

flights on the cities, to increases the overlap of the

images, regardless of the type of restitution.

The high overlap allows a higher probability of

successful matches but, at the same time, a small

base line involves in a low accuracy of the height, as

shown in the formula 4 and 8. This means that,

wanting to ensure a thorough and detailed DSM, the

flight plan has to be well designed.

Also it is necessary to make an observation of an

operational character: the high spatial resolution of

the images requires a low-level flight. The low

altitude atmospheric air is influenced by topography

and the local temperature field. This means that a

rotation of the aircraft can affect not only the quality

of photogrammetry, but also cause the observation

of elements not belonging to scene, that is, in other

words, can happen of to photograph the underside of

the aircraft or the aircraft hatch (Figure 10). All this

results in saying that the flight preparation requires

appropriate meteorological conditions and a proper

study of the flight planning.

Figure 10: Elements of the aircraft visible in the photo.

5 CONCLUSIONS

Through the use of image from digital oblique

camera system, the two software examined have

provided, in an automatic way, dense point clouds

and of high accuracy, of particular utility in many

fields of geomatics.

The approach SfM for the production of DSM

using aerial digital image of medium format is

possible, but unfortunately limited to a few images.

The division into sub-blocks of the entire aerial

survey has allowed to overcome, at least in part, this

problematic. However, the processing time becomes

too long if the work area is very extensive. Instead,

the dense Digital Surface Models obtained through

photogrammetry reconstruction based on GPU

technology and multi-core CPUs has allowed to

obtain not only accurate point clouds but enabled to

manage a block photogrammetric with a lot of

images.

ACKNOWLEDGEMENTS

The authors thank Giovanni Abate and Jacques

Markram, Leica Geosystems, for the supply of

images and Michael O'Sullivan, Simactive, company

for software availability.

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