Photogrammetric Modeling of Drapham Dzong (Bhutan) - Institute of ...

Photogrammetric Modeling of Drapham Dzong (Bhutan) 

Technical Report 

Maroš Bláha 

Henri Eisenbeiss 

Martin Sauerbier 

Armin Grün 

Institute of Geodesy and Photogrammetry 

Swiss Federal Institute of Technology Zurich 

i

Acknowledgements 

Acknowledgements 

We want to express our special gratitude to Eberhard Fischer, Peter Fux and Namgyel Tsering, 

who supported our work through their collaboration and man-power, as well as for the help 

during the preparation phase of the project and the UAV field mission. Without their support the 

UAV flight over Drapham Dzong would not be possible. Additionally, we are very grateful to 

Hilmar Ingensand, who enabled the data processing at the Institute of Geodesy and Photogrammetry 

(ETH Zurich). Furthermore, we thank Emil Siegrist from Omnisight, who supported us 

with the UAV field equipment. Finally, we thank Nusret Demir, David Novák and Fabio 

Remondino, who helped us during data processing. 

ii

Abstract 

Abstract 

The following report is based on the bachelor’s thesis of Maroš Bláha which was accomplished 

in spring 2010 at the Institute of Geodesy and Photogrammetry (ETH Zurich). 

The main topic of the Bhutan project is the photogrammetric modeling of the ruin of Drapham 

Dzong. The required input data was captured during the field campaign from 2 – 13 November 

2009 during which the ancient citadel was documented using the UAV (Unmanned Aerial Vehicle) 

system MD4-200 in addition to terrestrial images. This documentation was carried out 

within the framework of the first archeological project in the Kingdom of Bhutan. The goal is to 

generate a DSM (Digital Surface Model) of the ruin and its environment as well as an orthoimage 

of the corresponding area. These products can then be used for further projects mainly 

in the field of archeology. In addition, the aim was to visualize the castle complex and show the 

results at the Bhutan exhibition at the Rietberg Museum in Zurich (4 July – 17 October 2010). 

The first part of this report contains some basic theoretical principles which were applied during 

the data processing. Subsequently the photogrammetric evaluation of the image data is explained 

in detail. Thereby a distinction is made between UAV image processing and terrestrial 

image processing. The final results of these procedures are the orthoimages and an overview 

map of the main ruin structures in addition to a digital 3D model of further ruin objects adjacent 

to the main citadel. In the conclusion some further works relating to the photorealistic 3D model 

and the visualization products of the ruin are introduced. 

iii

Content 

Content 

Acknowledgements ....................................................................................................................... ii 

Abstract ........................................................................................................................................ iii 

Content ......................................................................................................................................... iv 

Figures ........................................................................................................................................... v 

Tables ........................................................................................................................................... vi 

1 Introduction ........................................................................................................................... 1 

1.1 About the Project .......................................................................................................... 1 

1.2 Research Goals .............................................................................................................. 2 

1.3 Outline ........................................................................................................................... 4 

2 Theory ................................................................................................................................... 6 

2.1 Mathematic Fundamentals ............................................................................................ 6 

2.2 The Interior Orientation ................................................................................................ 7 

2.3 The Exterior Orientation ............................................................................................... 8 

2.4 Image Orientation.......................................................................................................... 8 

2.5 Digital Terrain Models ................................................................................................ 10 

2.6 Orthoimage Generation ............................................................................................... 11 

3 Processing of the UAV Images ........................................................................................... 12 

3.1 Calibration of the Panasonic Lumix DMC-FX35 ....................................................... 12 

3.2 Preprocessing of the UAV Images .............................................................................. 13 

3.3 Orientation of the UAV Images .................................................................................. 16 

3.4 DSM and Orthoimage Generation .............................................................................. 19 

3.5 Results ......................................................................................................................... 22 

4 Processing of the Terrestrial Images ................................................................................... 30 

4.1 Calibration of the Nikon D3X ..................................................................................... 30 

4.2 Preprocessing of the Terrestrial Images ...................................................................... 31 

4.3 Orientation of the Terrestrial Images .......................................................................... 33 

4.4 Modeling of the Recorded Area .................................................................................. 36 

4.5 Results ......................................................................................................................... 37 

5 Further Works ..................................................................................................................... 39 

5.1 Digital Model of the Main Ruin Structures of Drapham Dzong ................................. 39 

5.2 Larger Area DSM and Orthoimage ............................................................................. 39 

5.3 Site Map ...................................................................................................................... 40 

6 Conclusions ......................................................................................................................... 42 

Bibliography................................................................................................................................ 43 

iv

Figures 

Figures 

Figure 1: Hill with the main ruin structures of Drapham Dzong. ............................................... 1 

Figure 2: Further ruin objects at the bottom of the hill. .............................................................. 2 

Figure 3: The MD4-200 during a test flight at the Jungfraujoch (Switzerland). ........................ 3 

Figure 4: GPS 500 during measurement of a GCP. .................................................................... 4 

Figure 5: Overview of the GCPs at the top of the hill. ............................................................... 5 

Figure 6: Simplified illustration of the central projection. ......................................................... 6 

Figure 7: Purpose of image orientation: determination of object points on the basis of 

intersecting rays (Photometrix, 2009). ......................................................................... 8 

Figure 8: Difference between a DTM and a DSM (Hanusch, 2009). ....................................... 10 

Figure 9: Indirect method of orthoimage generation (Photogrammetrie GZ, 2009). ............... 11 

Figure 10: Example of a picture which was used for the calibration of the Panasonic Lumix 

DMC-FX35. ............................................................................................................... 13 

Figure 11: Adjustment of the selected UAV images. ................................................................. 15 

Figure 12: Example of a manually measured tie point in LPS. .................................................. 17 

Figure 13: Distribution of the control points and tie points in the two image strips. ................. 18 

Figure 14: Top-down view of a DSM generated with SAT-PP out of a stereo scene of two 

images. The image section shows the area around the main tower Utze. .................. 20 

Figure 15: GRID of Drapham Dzong area generated with SCOP 5.4. ....................................... 21 

Figure 16: Manually measured 3D points in the Drapham Dzong ruin area. ............................. 21 

Figure 17: Orthomosaic of the area of Drapham Dzong (whole ruin complex) based on UAV 

images of the two image strips and the automatically generated DSM. .................... 23 

Figure 18: Orthomosaic of the area of Drapham Dzong (main citadel) based on UAV images 

of the two image strips and the automatically generated DSM. ................................. 24 

Figure 19: Orthomosaic of the area of Drapham Dzong (whole ruin complex) based on UAV 

images of the two image strips and the manually generated DTM. ........................... 25 

Figure 20: Orthomosaic of the area of Drapham Dzong (main citadel) based on UAV images 

of the two image strips and the manually generated DTM. ....................................... 26 

Figure 21: Orthomosaic of the area of Drapham Dzong (whole ruin complex) based on the 

UAV overview images. .............................................................................................. 27 

Figure 22: Orthomosaic of the area of Drapham Dzong (main citadel) based on the UAV 

overview images. ....................................................................................................... 28 

Figure 23: Overview map of the Drapham Dzong ruin area based on the second orthomosaic. 29 

Figure 24: Larger object of the additional ruin structures of Drapham Dzong. ......................... 31 

Figure 25: Putative water mill of Drapham Dzong. ................................................................... 32 

Figure 26: Camera positions and the corresponding photographs of the first terrestrial image 

group. ......................................................................................................................... 32 

Figure 27: Camera positions and the corresponding photographs of the second terrestrial 

image group................................................................................................................ 33 

Figure 28: Example of a manually measured tie point in PhotoModeler. .................................. 34 

Figure 29: Automatically generated tie points in two terrestrial images. ................................... 35 

Figure 30: Points measured in a clockwise sense on the top edge of a ruin wall. ...................... 37 

Figure 31: 3D model of the bigger ruin building resulting from the terrestrial image 

processing................................................................................................................... 38 

Figure 32: 3D model of the smaller ruin building resulting from the terrestrial image 

processing................................................................................................................... 38 

Figure 33: 3D model of the main ruin structures of Drapham Dzong. ....................................... 39 

Figure 34: View towards south on the surrounding area of Drapham Dzong. The arrow 

points on the ruin of the castle (Grün, et al., 2010). ................................................... 40 

Figure 35: Map of the archaeological site at Drapham Dzong (Grün, et al., 2010). .................. 41 

v

Tables 

Tables 

Table 1: Main parameters of the flight planning in Bhutan (Sauerbier and Eisenbeiss, 2010). 3 

Table 2: Technical parameters of the Panasonic Lumix DMC-FX35 which were used for 

the computation of the exact pixel size. ..................................................................... 12 

Table 3: Calibration results of the Panasonic Lumix DMC-FX35. ......................................... 13 

Table 4: Criteria of the image selection. ................................................................................. 14 

Table 5: Enhancement values of the UAV images in Photoshop. ........................................... 15 

Table 6: Adjustment of the coordinates of the principal point (Panasonic Lumix DMC- 

FX35 camera). ............................................................................................................ 16 

Table 7: A priori triangulation properties and a posteriori triangulation summary of the 

Table 8: 

two image strips. ........................................................................................................ 19 

Technical parameters of the Nikon D3X which were used for the computation of 

the exact pixel size. .................................................................................................... 30 

Table 9: Calibration results of the Nikon D3X. ...................................................................... 31 

Table 10: Enhancement values (in Photoshop) of the terrestrial images. ................................. 33 

Table 11: Adjustment of the principal point’s coordinates (Nikon D3X camera). ................... 34 

Table 12: Results of the image orientation of the terrestrial images. ........................................ 36 

vi

Introduction 

1 Introduction 

The first chapter gives a short overview about the ruin of Drapham Dzong. Furthermore, the allembracing 

project in context with the archeological excavation situated at Drapham Dzong is 

introduced (Meyer, et al., 2007). Finally, the works which were done in the context of the field 

campaign in Bhutan and the outline of this project are described. 

The photographs presented in this report were taken by the project team (Maroš Bláha, Peter 

Fux, Armin Grün and Henri Eisenbeiss) during the field work and data processing. 

1.1 About the Project 

The Ruin of Drapham Dzong 

The Drapham Dzong is a ruin complex located in the District of Bumthang in central Bhutan. 

According to oral tradition the ancient citadel was built in 15 th /16 th century. Nowadays there is 

an archeological excavation at the Drapham Dzong which is part of the first archeological project 

in the Kingdom of Bhutan. The main wall structures of the excavation, including the solid 

main tower (the so-called Utze), are located on the top of an approximately 80 m high hill in the 

valley and cover an area of 225 m x 100 m (figure 1). These structures are surrounded by watchtowers 

in the corners. Further ruin objects are situated on the eastern bottom of the rocky hill 

(figure 2). One of those objects adjoins a nearby brook. The structures at the foot of the hill 

represent the reputed economic center of the castle complex whereas the object next to the 

brook served most probably as water mill. In addition there are two fortified staircases which 

connect the different parts of the ancient castle complex. The whole area of the excavation lies 

at an altitude of almost 2900 m a. s. l. (Grün, et al., 2010; SLSA, 2013). 

Figure 1: Hill with the main ruin structures of Drapham Dzong. 

1

Introduction 

Figure 2: Further ruin objects at the bottom of the hill. 

The Documentation of Drapham Dzong 

The documentation of Drapham Dzong is a collective project by ETH Zurich, SLSA (Swiss- 

Liechtenstein Foundation of Archaeological Research Abroad) and Helvetas. Its aim is to generate 

a digital 3D model of the ruin which can be used for further works e.g. in archeology. The 

whole documentation project was carried out within the framework of the first archeological 

project in Bhutan. 

The here presented work shows the acquired data during the field campaign in Bhutan (November 

2009), the photogrammetric processing and the obtained results. In combination with some 

further works which are described in chapter 5, the results gathered in this project build the 

foundation of the visualizations at the exhibition ate the Rietberg Museum (Sacred art from the 

Himalayas, 2010). 

1.2 Research Goals 

During the field campaign from 2 - 13 November 2009, the whole ruin complex of Drapham 

Dzong was documented with photogrammetric recording methods. Thereby the main part of the 

excavation, located on the top of the hill, was recorded with the mini-UAV (Unmanned Aerial 

Vehicle) system MD4-200 from Microdrones. The MD4-200 is an electrically powered quadrotor 

with a diameter of less than 70 cm and an empty weight of 585 g. These parameters allowed 

for its easy transportation from Switzerland to Bhutan which was one of the reasons why this 

UAV system was chosen for this project. A second constitutive reason was the successful flight 

test at the Jungfraujoch (Switzerland) with the MD4-200 (figure 3). The height test was accomplished 

in summer 2009 and showed that the system copes well with the little air present at a 

height of up to 3000 m a.s.l. (Sauerbier and Eisenbeiss, 2010). 

2

Introduction 

Figure 3: The MD4-200 during a test flight at the Jungfraujoch (Switzerland). 

However, the autonomous flight modus was not working during the data acquisition in Bhutan. 

Therefore the photographs were acquired in the GPS-based assisted flight modus. At the same 

time, the image overlap was controlled by the operator on the ground control station, using the 

live-view modus (Grün, et al., 2010). Table 1 shows the summarized parameters of the flight 

planning in Bhutan. 

Table 1: Main parameters of the flight planning in Bhutan (Sauerbier and Eisenbeiss, 2010). 

Area 

Bhutan 

H g 

120 m 

f 

4.4 mm 

GSD 

4-5 cm 

v UAV 

2 m/s 

Flight mode 

Assisted 

Acquisition mode 

Cruising 

Size of the area 

350 m x 200 m 

p/q 75/75 % 

Due to technical problems with the remote control of the MD4-200 some parts of the excavation 

could not be recorded with aerial images. For this reason the ruin objects underneath the hill 

were documented with terrestrial pictures. 

Besides the acquisition of the image data, eleven GCPs (Ground Control Points) were surveyed 

during the field campaign in November 2009. These points were placed temporarily by the project 

team and served for the purpose of the absolute orientation in the photogrammetric processing. 

Seven out of the eleven GCPs covered the area with the main citadel whereas the remaining 

four were located at the foot of the hill. All GCPs were measured with two GNSS receivers 

GPS 500 from Leica Geosystems. In the process, one system was positioned as reference 

station while the other was used for the individual measurements of the numerous GCPs 

(Grün, et al., 2010). 

3

Introduction 

Figure 4: GPS 500 during measurement of a GCP. 

1.3 Outline 

The main objective was set on the reconstruction of several ruin structures. A further ambition 

was to generate an orthoimage of the main ruin complex situated on the hilltop. The basic principle 

of both procedures, the modeling as well as the orthoimage generation, is the photogrammetric 

processing of the image data which was acquired in Bhutan. In particular, the photogrammetric 

processing contains the following steps: 

 

 

 

 

 

Preprocessing of the images (UAV images and terrestrial images) 

Orientation of the images (UAV images and terrestrial images) 

DTM generation (UAV images and terrestrial images) 

Orthoimage generation (UAV images) 

Measurement of the ruin structures (terrestrial images) 

The raw data which was available for this project consisted mainly of the aerial images taken 

with the MD4-200 and the terrestrial photographs, which can be divided into two different 

groups. The first group consists of pictures which describe the ruin objects at the bottom of the 

hill with the main castle complex. The second group contains special photographs of the walls 

on the hilltop. These can be used for the purpose of texture mapping which is necessary in order 

to generate a photorealistic model of Drapham Dzong. The Panasonic DMC-FX35 was applied 

for the acquisition of the UAV image data. The aerial images feature a format of 3648 x 2736 

pixels. The terrestrial photographs were taken with the Nikon D3X and Nikon D2XS cameras. 

These pictures feature a maximum format of 6048 x 4032 pixels. Moreover, there are general 

documentation photographs available that can be applied for several purposes (e.g. presentation 

and poster of this project, exhibition at the Rietberg Museum, etc.). In addition to the image 

data, there are coordinates of eleven GCPs which were surveyed during the field campaign in 

Bhutan using Geographic Coordinates. The GCPs were measured with the static GNSS measurement 

system GPS 500 and feature an accuracy of 1 – 2 cm. Figure 5 shows the distribution of 

the seven GCPs on the top of the hill. The remaining four GCPs lie next to the hill in the area of 

the further ruin objects. 

4

Introduction 

Figure 5: Overview of the GCPs at the top of the hill. 

5

Theory 

2 Theory 

The purpose of this chapter is to explain several theoretical basics which were applied during 

this work. However, the theory described within this chapter is just a simplified extract of the 

extensive scientific literature. Nevertheless, it makes a contribution to the general understanding 

of the data processing which is topic of chapter 3 and 4. 

2.1 Mathematic Fundamentals 

Central Projection 

In order to reconstruct the shape and position of an object from photographs, it is necessary to 

know the geometry of the image forming system. Basically a photograph can be considered as a 

central projection of the recorded object. However, the central projection is just an approximation 

of the real world. For the purpose of high accuracy level results, it is necessary to incorporate 

additional sources of error which are caused by the lens system of the camera in question 

(Kraus, 2007; Grün, et al., 2010). 

Figure 6 shows a general, simplified model of the central projection. A more detailed description 

can be found in the book Photogrammetry – Geometry from Images and Laser Scans 

(Kraus, 2007). In addition, figure 6 contains couple values which are important in the context of 

metric cameras. The left and the right part of the illustration are not related directly to each other. 

Figure 6: Simplified illustration of the central projection. 

6

Theory 

The principle point is the perpendicular projection of the center of perspective on the image 

plane. The distance between the center of perspective and the principal point is the focal length 

of the applied camera. This parameter depends on the camera lens and the position of the image 

plane. The focal length affects the scale factor of the image directly. 

Collinearity Equation 

The relationship between an object point in the object coordinate system and an image point in 

the image coordinate system is defined by the collinearity equation. Since this equation is described 

in detail in the scientific literature, it is just mentioned here briefly in words. The elements 

of the collinearity equation are: 

The coordinates of the object point in the object coordinate system 

The rotation matrix 1 

The coordinates of the center of perspective in the object coordinate system 

The coordinates of the image point in the image coordinate system 

The coordinates of the principal point in the image coordinate system 

The scale factor 2 

The collinearity equation says that for each object point one certain image point exists. Contrariwise 

for each image point infinitely many object points exist. Therefore it is not possible to 

reconstruct an object from just one image. In order to solve this problem, it is necessary that 

each object is visible in at least two pictures (Kraus, 2007). 

2.2 The Interior Orientation 

Constitutive for the photogrammetric processing of metric images are the values of the interior 

orientation of a camera. These values allow the reconstruction of the spatial bundle rays, which 

are required for the generation of a digital 3D model from images. The rays are defined by the 

center of perspective and the coordinates of points in the image coordinate system. Hence, the 

parameters of the interior orientation are: 

 

 

The focal length 

The coordinates of the principal point in the image coordinate system 

The meaning of the focal length and the coordinates of the principal point have already been 

explained above. In order to achieve results on a high accuracy level it is important to consider 

additionally the lens distortion values. The influence of these parameters can be generally described 

as follows: in theory an object point, the center of perspective and the corresponding 

image point form a spatial ray which is assumed to be a straight line. Due to radial symmetric 

distortion introduced by the camera lens, the ray deviates from a straight line. This effect is 

caused because of the changing refraction of the light rays at the objective lens. For the purpose 

of an accurate photogrammetric processing, the radial symmetric distortion has to be considered 

in the interior orientation (Photometrix, 2009). In some cases even more parameters, e.g. the 

decentering distortion, are determined in addition to the radial symmetric distortion. 

In the case of metric cameras, the parameters of the interior orientation are provided by the 

camera manufacturer in a calibration protocol. Contrariwise the interior orientation of off-theshelf 

cameras is usually not known and has to be determined. This procedure is called camera 

calibration. 

1 The rotation matrix describes the orientation of an image in the object coordinate system. 

2 The scale factor is defined among others by the focal length of the concerning camera. In the case of 

aerial images the scale factor is defined as ratio of the focal length to the flying height. 

7

Theory 

2.3 The Exterior Orientation 

The parameters of the exterior orientation describe the position of an image in object space. 

With the aid of these parameters, combined with the values of the interior orientation, it is possible 

to reconstruct an object in its real shape and position. Each image features six parameters 

of the exterior orientation: 

The spatial coordinates of the center of perspective in the object coordinate system 

The angles of rotation with respect to the axes of the object coordinate system 3 

There are basically two different methods for determining the parameters of the exterior orientation: 

the direct and the indirect georeferencing. If a GPS/IMU 4 system is used while taking the 

images, it is possible to measure the values of the exterior orientation directly during the recording 

process. Therefore this procedure is also known as direct georeferencing. Otherwise, if no 

GPS/IMU is available, the parameters of the exterior orientation have to be determined using 

control points. Since this is an indirection, this method is called indirect georeferencing (Kraus, 

2007). 

2.4 Image Orientation 

Image orientation is a prerequisite for the generation of 3D models from images. The purpose of 

this procedure is to re-establish the geometrical relations of the central projection in order to be 

able to reconstruct an object in its real shape and position. For this re-establishment, the parameters 

of the interior and exterior orientation are required. The purpose of the image orientation is 

depicted in figure 7. Thereby the descriptions S1, S2 and S3 represent the particular centers of 

perspective and the camera locations respectively. 

Figure 7: Purpose of image orientation: determination of object points on the basis of intersecting rays 

(Photometrix, 2009). 

3 For the angles of rotation often the identifiers ω, φ and κ are used. 

4 IMU stands for Inertial Measurement Unit. 

8

Theory 

There are several methods of image orientation. One often applied method is the block triangulation. 

Since this procedure was also used for the UAV image orientation, it is briefly explained 

below. To describe all other procedures in detail would excess the frame of this report. A circumstantial 

description of different orientation methods including the exact mathematical backgrounds 

can be found in the book Photogrammetry – Geometry from Images and Laser Scans 

(Kraus, 2007). However, the image orientation can be split basically into two procedures – the 

relative and the absolute orientation – which are explained hereafter. For the purpose of a good 

comprehensibility, it is assumed that only two images are oriented. Furthermore, the interior 

orientation is assumed to be known while the exterior orientation is computed by the way of 

indirect georeferencing. This means that overall twelve parameters of the exterior orientation 

need to be determined (six for each image). It should be mentioned that in reality there are usually 

more than two images which are processed during image orientation. 

Relative Orientation 

In the first part of image orientation the relative position and orientation of the images are determined. 

This process is called relative orientation. The relative orientation is conducted by the 

measuring of tie points in the pictures. Tie points represent the intersection of homologous rays 5 

and are usually significant, static points equally distributed over the overlapping area of the two 

images. The result of the relative orientation is a stereo model 6 of the recorded area in the model 

coordinate system 7 . Though, the stereo model features an arbitrary scale, position and orientation 

in space. Hence it follows that it does not have any reference with a global coordinate system. 

The determination of these parameters is part of the absolute orientation. 

The mathematical basis for the relative orientation is the coplanarity condition which is defined 

by nine parameters. Since the relative orientation is defined by just five parameters, the remaining 

four parameters can be fixed arbitrarily. For that reason there are different possible methods 

of the relative orientation, depending on which parameters are fixed and which ones are computed. 

Absolute Orientation 

In the part of the relative orientation, five of overall twelve parameters of the exterior orientation 

were determined. The remaining seven parameters have to be computed in the absolute 

orientation process. These seven parameters describe a 3D similarity transformation which is 

applied on the stereo model from the relative orientation. Through this transformation the coherence 

between the model coordinate system and the object coordinate system accrues whereby 

the stereo model regains its original scale, position and orientation. 

As previously mentioned above, in this case the absolute orientation is carried out through control 

points. Control points are spots whose coordinates are known in a superior coordinate system, 

mostly a national coordinate system. There are three different sorts of control points: 

 

 

 

Full control point (X, Y and Z are known) 

Horizontal control point (X and Y are known) 

Height control point (Z is known) 

Since the 3D similarity transformation is defined by seven parameters, there are at least seven 

pieces of control point information required in order to accomplish an absolute orientation. 

Thereby four out of the seven pieces of control point information must be horizontal infor- 

5 Homologous rays are defined by image points of an identical object point and intersect in the corresponding 

point in object space. 

6 A stereo model in the model coordinate system is defined by all intersections of homologous rays. The 

precondition for such a model is the successful relative orientation of the images. 

7 The model coordinate system lies in an arbitrary position in space. It features no reference to a subordinate 

coordinate system. 

9

Theory 

mation. Therefore it follows that for an absolute orientation at least two horizontal control 

points and three height control points or two full control points and one height control point 

must be available. 

Block Triangulation 

In this procedure, the relative and absolute orientation are calculated in one step. Hereby the 

coherence between the image coordinates and the object coordinates is determined directly and 

without a loop way over the model coordinates. The unknown values of the block triangulation 

are the parameters of the exterior orientation. Furthermore, the object coordinates of all measured 

tie points also need to be determined. As observations serve the image coordinates of the 

control points and of the tie points. It is presumed that the coordinates of the control points are 

known. The adjustment principle can be described in the following way: the homologous rays 

are dislocated and rotated in such a way that they intersect the tie points as precisely as possible 

and that they converge with the control points as precisely as possible (Kraus, 2007). 

2.5 Digital Terrain Models 

A digital terrain model (DTM) describes the spatial characteristics of the topography. Thereby 

the spatial distribution of the terrain is usually represented by a horizontal coordinate system (X, 

Y) whereas the terrain characteristic is given by the corresponding Z coordinate. Moreover, a 

distinction can be made between 2.5D and 3D data. In the case of 2.5D data, there is exactly one 

corresponding Z value for each X, Y tuple. On the contrary, in 3D models it is possible that an 

X, Y tuple has several Z values. 

There are different possibilities in order to describe the topographic characteristics. Essentially 

the ground surface level can be described in two different ways: 

 

 

Models which represent the bare topography without any objects like e.g. trees or buildings. 

The generic term for such types of models is DTM (Digital Terrain Model). 

Models which describe the earth’s surface including objects like trees or buildings. These 

models can be generalized under the term DSM (Digital Surface Model). 

Figure 8 illustrates the difference between a DTM and a DSM. 

Figure 8: Difference between a DTM and a DSM (Hanusch, 2009). 

A DSM can be generated through both manual measurement as well as automatic DSM generation. 

In the following, the latter procedure is briefly described. The automatic DSM generation 

is based upon corresponding descriptions of identical objects. This process is also called matching. 

Relatively oriented images are a prerequisite for a successful matching. The final result is a 

3D point cloud. However, the spots within the point cloud are distributed irregularly which is 

one of the reasons why it is not appropriate for the purpose of visualization. Therefore, the point 

cloud is often processed again into a more demonstrative data model. Two representations often 

encountered are: 

10

Theory 

 

 

GRID: In this representation the spatial data is available in a regular raster. The raster follows 

from the interpolation 8 into the original points of the 3D point cloud. 

TIN (Triangulated Irregular Network): In a TIN the points of the 3D point cloud are meshed 

to not overlapping triangles without any gap. The benefit of a TIN compared with a GRID 

is that it is possible to visualize more complex surfaces. 

2.6 Orthoimage Generation 

An orthoimage corresponds to a parallel projection with a consistent scale factor in the whole 

image. In contrast to a common photograph, which is generally an inclined central projection, it 

features no offset stacking, thus allowing reliable distance measurements. Additionally, it is 

possible to superpose an orthoimage on an ordinary map correctly. Typical application areas for 

orthoimages are e.g. photorealistic maps or visualization products in general. 

In order to be able to generate an orthoimage the orientation data of an image (interior and exterior) 

and the associated DTM are required. There are two methods of orthoimage generation: the 

direct and the indirect method. From a mathematical point of view, both techniques are based 

upon the same foundations. Since the indirect method is often preferred, only this will be explained 

in the following section. The main procedures of an indirect orthoimage generation are: 

 

 

 

 

Firstly, an X, Y matrix consisting of quadratic elements is spread out on the layer of the 

orthoimage. 

In the next step, the corresponding Z value for each X, Y tuple is computed using the available 

DTM. 

Afterwards, the collinearity equation is implemented as follows: the previously calculated Z 

value and the center of perspective of the image build a line which intersects with the image 

plane. This intersection defines the image point (x’, y’) which corresponds to the X, Y tuple 

in the orthoimage. 

Finally, the grey tone of the above determined image point (x’, y’) is interpolated onto the 

corresponding X, Y tuple in the orthoimage. 

Figure 9 shows the process of indirect orthoimage generation in a simplified manner. The identifiers 

of the indirect orthoimage generation process described above are related to those in figure 

9. 

Figure 9: Indirect method of orthoimage generation (Photogrammetrie GZ, 2009). 

8 The purpose of the interpolation process is to determine a closed surface by considering the original 

spots of the 3D point cloud. This can be accomplished in two different ways: Interpolation or Approximation. 

11

Processing of the UAV Images 

3 Processing of the UAV Images 

The following chapter describes all steps which were accomplished during the processing of the 

UAV image data. Thereby, most of the applied processes are based on the theoretical basics 

described in chapter 2. Furthermore, this work step can be divided into two parts: processing of 

two image strips and processing of the overview images. Unless particularly mentioned, the 

procedures in both parts were identical. Three orthoimages and an overview map of the main 

ruin complex of Drapham Dzong resulted from the UAV image processing. 

3.1 Calibration of the Panasonic Lumix DMC-FX35 

General Information about the Panasonic Lumix DMC-FX35 

For the acquisition of the UAV images, the camera model Panasonic Lumix DMC-FX35 was 

used. The DMC-FX35 is a compact camera with a zoom lens and a maximum format of 

3648 x 2736 pixels. As already mentioned above, the documentation images of the Drapham 

Dzong also feature this resolution. Additional functions of the DMC-FX35 include an image 

stabilizer, a shutter of up to two milliseconds and a live-view video function (Sauerbier and 

Eisenbeiss, 2010). 

Computation of the Exact Pixel Size 

Constitutive for an accurate camera calibration is the value of the exact pixel size. In fact, a 

wrong pixel size can lead to satisfactory calibration results, but the calculated focal length of the 

concerned camera will be incorrect. As a result, a wrong pixel size causes a scaling effect 

(Photometrix, 2009). Since the pixel size was not provided by the camera’s manufacturer, it was 

necessary to calculate this value in a roundabout way using the available technical data. 

On the www.dpreview.com website, among others, the following technical parameters concerning 

the DMC-FX35 can be found: 

Table 2: Technical parameters of the Panasonic Lumix DMC-FX35 which were used for the computation of 

the exact pixel size. 

Sensor photo detection 

Sensor width 

Sensor height 

10.7 megapixel 

6.13 mm 

4.6 mm 

Using the data in table 2 it was possible to calculate the pixel size of the Panasonic Lumix 

DMC-FX35. Thereby it had to be foreclosed that a pixel was generally assumed to be quadratic. 

The first step in this procedure was the calculation of the sensor size, which was obtained 

through the multiplication of the sensor width (6.13 mm) and sensor height (4.6 mm). Hence a 

sensor size of 28.198 mm 2 followed. In order to ascertain the size of one pixel, the 

28.198 mm 2 were divided by 10700000 which resulted in a pixel size of 2.6353e-6 mm 2 . The 

square root results to a pixel size of 1.62 microns (same for x and y direction). 

Calibration of the Panasonic Lumix DMC-FX35 

The calibration of the Panasonic DMC-FX35 was performed with the software package iWitness. 

Via the AutoCal command, iWitness computed an automated camera calibration by using 

twelve color coded targets arranged in the shape of a rectangle (figure 10). Thereby two of the 

targets need to be elevated. The reason therefore is that the height is correlated directly with the 

focal length. An accurate and reliable camera calibration requires images taken from different 

viewpoints, rotations and distances to the object. Obviously it is necessary that the targets are 

12


visible in the recorded pictures. Since the DMC-FX35 was already stored in the iWitness database, 

no input values needed to be entered for an automated camera calibration. The only parameter 

which was saved wrongly and needed to be corrected was the above computed pixel 

size (1.62 microns). 

Figure 10: Example of a picture which was used for the calibration of the Panasonic Lumix DMC-FX35. 

For the calibration of the Panasonic DMC-FX35 21 photographs of the targets were taken from 

different positions. The results of the calibration including all significant camera parameters are 

listed in table 3. Furthermore, the parameters K1, K2 and K3 in table 3 represent the radial distortion 

of the camera lens. 

Table 3: Calibration results of the Panasonic Lumix DMC-FX35. 

Resolution, width 

Resolution, height 

Pixel width 

Pixel height 

Focal length 

Coordinate xp, principal point 

Coordinate yp, principal point 

K1 

K2 

K3 

3648 pixels 

2736 pixels 

1.6 microns 

1.6 microns 

4.3968 mm 

0.0078 mm 

0.0414 mm 

-1.0888e-003 

3.6922e-004 

-1.2841e-005 

3.2 Preprocessing of the UAV Images 

Image Selection 

Before image orientation was carried out, the UAV photographs were preprocessed. The first 

step in this context was the selection of appropriate images according to the criteria: image 

sharpness, lighting conditions, orientation and overlapping areas of the pictures. Since all fol- 

13


lowing procedures depended on this image selection, it was important for it to be completed 

very carefully. Thereby highest priority was placed on image sharpness and lighting conditions. 

Furthermore, the pictures needed to have a sufficient overlapping area of 60% or more. In order 

to clarify the meaning of the above mentioned criteria, the following table was created: 

Table 4: Criteria of the image selection. 

Criterion Appropriate picture(s) Inappropriate picture(s) 

Image sharpness 

Lighting Conditions 

Orientation 

Overlapping area 

During the field campaign in November 2009, 202 aerial images overall were taken with the 

MD4-200. The data content of these images is the hill with the main part of the ruin complex. 

The aerial photographs can be divided into three different groups. The first group contains overview 

images which served mainly for the purpose of the flight planning. Each one of the remaining 

two groups forms one image strip whereas the strips proceed parallel to each other in a 

northeast to southwest direction. At first all blurred and overexposed pictures were deselected. 

In the next step the photographs with an overlapping area of approximately 60% or more in 

addition to an appropriate orientation were sorted out. Thus, with regard to the photogrammetric 

processing and the final products, it was important that the selected images covered the whole 

14


hill including the main structures of Drapham Dzong. The result of the image selection were 6 

overview images and two image strips consisting of 8 and 10 images for strip one and two respectively. 

The following figure shows the adjustment of the two image strips which were used 

for the image orientation (Grün, et al., 2010). It should be mentioned that in each of the two 

strips an additional two pictures were deactivated because they interfered with the results of the 

image orientation in LPS. 

Image strip 1 

Image strip 2 

Figure 11: Adjustment of the selected UAV images. 

Image Enhancement 

Despite the above mentioned image selection procedure, the selected pictures still featured some 

shortcomings in image quality (e.g. overexposure). Therefore the second part of the preprocessing 

was the enhancement of the selected images, such that these shortcomings could be 

reduced. This step was only applied to the two image strips. It was accomplished with Adobe 

Photoshop and included image contrast and brightness enhancement. Since the images of the 

two strips were acquired on different days and day time, they featured different lighting conditions. 

For this reason the strips were edited individually. The exact enhancement values of Photoshop 

which were applied to the image strips can be extracted from the following table (Grün, 

et al., 2010): 

Table 5: Enhancement values of the UAV images in Photoshop. 

Contrast 

Brightness 

Strip 1 (2 nd flight) -25 -35 

Strip 2 (2 nd flight) -25 -35 

Strip 2 (3 rd flight) -15 -10 

Image Caption 

In order to simplify the following procedures, it was necessary to develop a systematic caption 

code for the selected images of the two image strips. This meant that each name should be significant 

and contain the strip number and the enhancement procedures which were applied to 

the picture in question. Also, the original name of the image should be present in the new caption. 

The outcome of these requirements was the following code, which is explained here in two 

examples: 

 

 

1B_1S_2F_P1030955_Brightness_Contrast_gedreht 


15


The letters B, S and F stand for the German words Bild (Eng. image), Streifen (Eng. strip) and 

Flug (Eng. flight). Hence it is possible to conclude that both examples represent the first photograph 

per strip, whereas the examples were taken during the second and third flight respectively. 

The numeration within both strips proceeds from northeast to southwest. The original name 

of the image as it was given by the camera then follows. Last but not least the steps which were 

applied to the picture during image enhancement are retained. Here the German word gedreht 

(Eng. rotated) informs us that the selected images were rotated 90 degrees clockwise. The rotation 

of the images does not affect the results of the following image orientation. However, this 

step helps with regard to the measurement of tie points in the photogrammetric processing with 

LPS (Leica Photogrammetry Suite). Since the number of the selected overview images was 

substantially lower than that of the image strips, it was not necessary to apply a caption code in 

this case. 

3.3 Orientation of the UAV Images 

The orientation of the UAV image data was accomplished with the software Leica Photogrammetry 

Suite 9.2 (LPS). Before one was able to properly start processing the pictures some preparation 

work was necessary. The first step (after the LPS project had been set up and the UAV 

images imported) at this stage was to import the parameters of the interior orientation. Since the 

UAV images of the two image strips were rotated 90 degrees clockwise, it was necessary to 

adjust the coordinate values of the principle point. Table 6 shows the relation between the old 

and the new coordinates of the principal point. In the case of the overview images it was not 

necessary to convert the coordinates of the principal point. 

Table 6: Adjustment of the coordinates of the principal point (Panasonic Lumix DMC-FX35 camera). 

Old (iWitness) 

New (LPS) 

x-coordinate 0.0078 mm 0.0414 mm 

y-coordinate 0.0414 mm -0.0078 mm 

The remaining calibration parameters (focal length and radial distortion) were taken directly 

from the iWitness calibration report without any changes. The calculation of image pyramids 

subsequently followed. Here the resolution was modulated on three zoom levels which are used 

in LPS. 

Once all preparation work was completed, it was possible to start with the image orientation. 

Firstly, the coordinates of the surveyed GCPs were imported into the LPS project (in this case 

the GCPs represent full control points). Because these coordinates were not available in the 

required UTM format, they first had to be converted. This conversion was performed with the 

online software Geographic/UTM Coordinate Converter (Taylor, 2013). It was then possible to 

import the coordinates of the ground control points and measure them in the LPS Classic Point 

Measurement Tool. In order to avoid confusion in the subsequent image processing it was important 

that the measured points had a systematic ID number. For this purpose the following ID 

code was applied: 

 

 

 

 

1XXX for all GCPs 

2XXX for all tie points within the first image strip 

3XXX for all tie points within the second image strip 

4XXX for all tie points between the first and the second image strip 

The next step in the image orientation - measurement of the tie points – was also carried out in 

the Classic Point Measurement Tool of LPS. Thereby the tie points in all overlapping areas 

within the image strips (i.e. the 2XXX and 3XXX points) were firstly measured. This part consisted 

mainly of the manual tie point measurement and the automatic tie point generation. For 

the purpose of a good automatic tie point generation it was important that the manually meas- 

16


ured tie points were distributed equally in the overlapping areas of the pictures. This was necessary 

both for the position as well as for the height. For the manually measured tie points clear 

spots, e.g. corners of walls, were often used. Figure 12 shows a typical example of a manually 

measured tie point in LPS. Also visible in figure 12 is the symmetric division of the screen into 

two halves. Each of them represents an aerial image in three different zoom levels. Related to 

the left image the display window in the top left corner shows the lowest zoom level whereas 

the window to the right shows the highest zoom level. The big image section below shows the 

middle zoom level of the left image. 

Figure 12: Example of a manually measured tie point in LPS. 

In this project approximately 6 points were measured manually in each overlapping area. These 

spots were used as initial measurements for the automated tie point generation (Grün, et al., 

2010). The automated tie point generation is based on a matching procedure, whereby the coefficient 

limit was set at 0.95 here. By the use of such a high coefficient limit, the probability of 

blunders is reduced. Additionally, 25 was entered as the intended number of points per image. 

The remaining adjustments of the automated tie point generation in LPS were kept at their default 

values. Despite the high coefficient limit all automatically generated tie points were controlled 

on blunders. In some cases LPS was not able to generate points. Forested and underexposed 

areas particularly featured problems in this aspect. In these areas additional tie points 

were measured manually. Finally, it should be mentioned that many tie points appeared in more 

than one overlapping area. All these points were measured in a high image ray number. This led 

to a more stable image block and also to better results in the image orientation. 

By the end of the tie point measurement all 4XXX points had been quantified. For this step the 

automatic tie point generation was not applied. Only a few tie points were measured in the overlapping 

area of the two image strips. The requests for the distribution of these points were the 

same as in the case of the 2XXX and 3XXX points. Finally there were ~550 (image strips) and 

~100 (overview images) measured tie points respectively. An overview of the measured tie 

points is illustrated in figure 13. The IDs of the several points have been left out as they would 

interfere with the clear illustration. 

17


Figure 13: Distribution of the control points and tie points in the two image strips. 

The next step was to calculate the block triangulation. Since in this case the working images are 

aerial photographs, this procedure is also called aerial triangulation. In contrast to the overview 

images which all were oriented in one step, the two image strips were at first oriented separately. 

During this process some of the photographs caused difficulties. Therefore these images 

were deactivated and not used for the orientation. However, this approach was only possible 

because the remaining photographs satisfied the requirement for a sufficient overlapping area. 

The images which were not used for the image orientation are: 

 

 

 

 





Afterwards both image strips were oriented together. Not all tie points and control points were 

used for the whole orientation process. The reason for this is that some points were measured 

imprecisely which would reduce the accuracy of the block triangulation. This especially affects 

the manually measured spots. Since in this project enough tie points were measured so that the 

mathematical model of the block triangulation was over-determined, it was possible not to use 

some of the redundant points for the image orientation. The following table shows the triangulation 

properties which were applied to the block triangulation and the results of the calculated 

block triangulation. 

18


Table 7: A priori triangulation properties and a posteriori triangulation summary of the two image strips. 

A priori triangulation properties 

Image point standard 0.6 pixels [x] 0.6 pixels [y] 

deviation (image 

coordinates) 

GCP standard deviation 

0.03 m [X] 0.03 m [Y] 0.03 m [Z] 

(object coordi- 

nates) 

A posteriori triangulation summary 

Total image unitweight 

0.5411 pixels 

RMSE 

Root mean square 0.0748 m [X] 0.0666 m [Y] 0.0828 m [Z] 

error of the ground 

coordinates 

Root mean square 

error of the image 

coordinates 

0.8126 pixels [x] 0.6570 pixels [y] 

An important value in the triangulation summary is the total image unit-weight RMSE; RMSE 

stands for Root Mean Square Error. This parameter describes the total root mean square error 

for the triangulation and represents the global quality of the block triangulation. 

3.4 DSM and Orthoimage Generation 

DSM Generation with SAT-PP 

Following the orientation of the UAV images in LPS it was possible to generate a DSM of the 

recorded area. In a first approach, the software SAT-PP (Satellite Image Precision Processing) 

was applied in order to realize this step. This software was developed by the Group of Photogrammetry 

and Remote Sensing at ETH Zurich (Zhang 2005). The main element of SAT-PP is 

the matching algorithm for DSM generation. Here the software measures identical points in 

overlapping images which were taken from different positions. The basis for this process is a 

multi-image least-squares matching (LSM) for image features like points and edges. For the 

purpose of accurate and reliable results additional geometrical constraints are applied. The result 

is a dataset of 3D points and edges which finally are combined to a raster dataset. This dataset 

describes the ground surface covered by the stereo scene 9 of the images. More details concerning 

the above mentioned algorithm can be found in the scientific literature (Zhang, 2005; Grün, 

et al., 2010). 

In this project, a separate DSM was generated for each stereo scene but within both image strips 

only. In this way it was possible to extract the best parts from each DSM and combine them at 

the end. The result of this procedure was 5 DSMs for the first image strip and 7 DSMs for the 

second one. For the DSM generation the SAT-PP function Image Matching & DEM Generation 

for Image Stereo Pair was used. In the process Mass Points and Line Features were applied as 

Matching Parameters. An example of a DSM generated with the software SAT-PP is illustrated 

in figure 14. 

9 A stereo scene accrues through overlapping images and allows a spatial consideration and processing of 

the image data. 

19


Figure 14: Top-down view of a DSM generated with SAT-PP out of a stereo scene of two images. The image 

section shows the area around the main tower Utze. 

Once all DSMs were generated they were loaded into the software Geomagic Studio 9. The 

reason for this proceeding was to choose the best parts of the particular point clouds and to 

combine them to one conclusive DSM which represents the recorded area. In order to avoid 

noise effects, caused by orientation effects, in the following interpolation it was aimed that the 

overlapping areas of the several models were as small as possible. On the other side it was important 

to avoid holes between the different DSMs. Considering these two criteria all point 

clouds were cropped and finally merged into a surface consisting out of 2,329,211 triangles. The 

merging process served mainly for the purpose of fitting the different point clouds in the overlapping 

areas to each other. In the end the 3D coordinates of 2,200,000 points of the merged 

model were exported and used for the interpolation into a GRID. This work step is described in 

the next paragraph. 

The generation of a GRID was realized with the program SCOP 5.4 (Inpho). SCOP allows to 

calculate a GRID with different interpolation methods. In this case two different GRID versions 

were generated: One with the classic prediction method and another with the robust filtering 

method. In both cases the GRID width was set to 0.25 m. In terms of the orthoimage generation 

the classic prediction conducted to an unusable result. Therefore this procedure is not exemplified 

here in detail anymore. Also the result of the robust filtering (figure 15) featured some 

shortcomings, but it was suitable for the following procedures. Some important steps of the 

robust filtering strategy are: Eliminate buildings, filter, interpolate, fill void areas, classify, edit, 

thin out and sort out. After the interpolation the GRID was loaded back into the final LPS project 

of the UAV images. There it was used for the calculation of an orthomosaic 10 . 

10 An orthomosaic is a combined picture consisting out of several orthoimages. 

20


Figure 15: GRID of Drapham Dzong area generated with SCOP 5.4. 

An additional DTM was measured manually by identifying corresponding points in the stereo 

scenes of the processed UAV images. The purpose of this proceeding was to improve the shortcomings 

which occurred in the automatic DSM generation. These insufficiencies induced geometrical 

problems in the following orthoimage rectification (see also chapter 3.5). For the manual 

DTM generation the stereo tool of LPS emerged as appropriate in order to measure the 3D 

coordinates in the Drapham Dzong ruin area (figure 16). The interpolation of these points into a 

GRID was carried out analogically to the automatic DSM generation. Overall, the new GRID 

consists of 354,144 3D points. Finally, this DTM was also used for the rectification of an additional 

orthoimage. 

Figure 16: Manually measured 3D points in the Drapham Dzong ruin area. 

Orthoimage Generation 

Based on the UAV images of the two image strips and the above calculated GRIDs two orthomosaics 

and an overview map of the ruin complex were produced. Furthermore, a third orthomosaic 

was generated based on the 6 overview images. Therefore a source DTM was used 

21


which was generated by Fabio Remondino at the Bruno Kessler Foundation in Trento (Italy). 

All three steps were accomplished using the LPS Mosaic Tool. Thereby the output cell size of 

the orthoimage was set to 5 cm. The cutlines for the intersections of the images were generated 

automatically by means of the Automatically Generate Seamlines for Intersections function of 

LPS. Moreover, the seams of the orthomosaic based on the manual measured DTM were edited 

additionally. Besides, no color corrections were applied in this process. The contrast and brightness 

adjustment was later carried out in Photoshop. This step was accomplished analog to the 

image enhancement mentioned above. The outcomes of the orthoimage generation are presented 

in the next chapter. 

3.5 Results 

The results of the UAV image processing are three orthomosaics and an overview map of the 

main ruin structures of Drapham Dzong. In the following the first image refers to the orthomosaic 

based on automatically generated DSM while the second one refers to the orthomosaic 

based on the manually measured DTM. The image source of both orthomosaics are the two 

strips. Finally, the third mentioned orthomosaic is based on the overview images. 

In its current status the first image features some shortcomings in geometry and radiometry 

(figure 17 and 18). While the radiometric difficulties were mainly caused by different lighting 

conditions in the pictures, the origin of the geometric problems is the automatically calculated 

DSM (see also chapter 3.4). The radiometric problems can be rectified with the LPS Color Correction 

functions, in addition to the general image adjustment tools in Photoshop. More problematical 

was the elimination of the shortcomings in the DSM. Since the ground surface of the 

Drapham Dzong area is very complex (it includes vegetation, walls, buildings without roofs, 

holes from the excavation, etc.), it is very difficult to produce a DSM which leads to an errorfree 

orthoimage. One possibility in order to solve this problem is to edit the DSM manually and 

generate a true orthoimage based on the measured ruin structures in the digital 3D model of 

Drapham Dzong (see also chapter 5.1). A similar approach was realized in conjunction with the 

second generated orthomosaic (figure 19 and 20). However, in this case a completely new DTM 

was measured manually instead of editing the existing DSM of the first image. The result 

showed a clear improvement considering the geometric and radiometric characteristics. 

Since the overview images have been acquired from a higher altitude (approximately 120 m) 

than the UAV images of the two strips, the third orthomosaic features a lower ground resolution 

than the first and second one (figure 21 and 22). In addition there are geometrical problems in 

the border areas of this image. Those are caused mainly by shortcomings in the source DTM. 

However, the textures in the second orthomosaic are better compared to the first two. The reason 

therefore are beneficial lighting conditions in the here applied aerial images. 

Comparing all three orthomosaics with each other one can conclude that overall the radiometric 

and geometrical aspects appear best in the second image. Decisive for this result are among 

other things the manually measured DSM in addition to the manually edited seamlines. For this 

reason, an overview map including contour lines and a coordinate grid was generated out of the 

second image (figure 23). The software package which was used for this proceeding was 

ArcGIS. 

22


Figure 17: Orthomosaic of the area of Drapham Dzong (whole ruin complex) based on UAV images of the two 

image strips and the automatically generated DSM. 

23


Figure 18: Orthomosaic of the area of Drapham Dzong (main citadel) based on UAV images of the two image 

strips and the automatically generated DSM. 

24


Figure 19: Orthomosaic of the area of Drapham Dzong (whole ruin complex) based on UAV images of the two 

image strips and the manually generated DTM. 

25


Figure 20: Orthomosaic of the area of Drapham Dzong (main citadel) based on UAV images of the two image 

strips and the manually generated DTM. 

26


Figure 21: Orthomosaic of the area of Drapham Dzong (whole ruin complex) based on the UAV overview 

images. 

27


Figure 22: Orthomosaic of the area of Drapham Dzong (main citadel) based on the UAV overview images. 

28


Figure 23: Overview map of the Drapham Dzong ruin area based on the second orthomosaic. 

29

Processing of the Terrestrial Images 

4 Processing of the Terrestrial Images 

The topic of chapter 4 is the processing of the terrestrial images. Most procedures described 

hereafter were done for the same purpose as in the case of the UAV images. The theoretical 

backgrounds and details in context with the here mentioned methods can be found in chapter 2 

and 3. The result of the terrestrial image processing was a digital 3D model of two ruin objects 

situated at the foot of the hill with the main ruin. 

4.1 Calibration of the Nikon D3X 

General Information About the Nikon D3X 

For the documentation of the putative economic center of Drapham Dzong located on the eastern 

foot hill the Nikon D2XS and D3X camera models were used. Since the images of the 

D2XS were not used for the processing of the terrestrial images this camera is not mentioned in 

detail here. The Nikon D3X is a high-resolution reflex camera (SLR) with a maximum format 

of 6048 x 4032 pixels which was also applied to the terrestrial photographs of Drapham Dzong. 

It is possible to use the Nikon D3X in combination with a GPS receiver which allows to measure 

the position from which the pictures were taken. In addition the exchangeability of the 

lenses increases the flexibility of the camera and allows the Nikon D3X to be optimally applied 

in different situations. 

Computation of the Exact Pixel Size 

The exact pixel size of the Nikon D3X was computed in a different manner to that of the DMC- 

FX35. Additionally, in the terrestrial case the source of the required technical parameters was 

the webpage www.dpreview.com, and this information is also confirmed by other sources. The 

values which were used for the determination of the Nikon D3X pixel size are listed in table 8. 

Table 8: Technical parameters of the Nikon D3X which were used for the computation of the exact pixel size. 

Maximal format 

Sensor width 

Sensor height 

6048 x 4032 pixels 

35.9 mm 

24 mm 

The exact pixel size of the Nikon D3X was computed as follows: 

Firstly, the sensor width (35.9 mm) was divided by the according number of pixels (6048). 

Hence this resulted in a pixel side length of 5.94 microns. 

Secondly, the analogue calculation was performed for the sensor height (24 mm) with its 

corresponding number of pixels (4032). The result was a pixel side length of 

5.95 microns which affirms the result obtained above. Additionally, this fact confirms the 

general assumption of quadratic pixels in chapter 3.1. 

Calibration of the Nikon D3X 

Similar to the Panasonic Lumix DMC-FX35, the Nikon D3X was also calibrated using the 

iWitness program. Since this process is analogous to the calibration of the DMC-FX35, it is not 

explained here in any more detail. However, one constitutive difference was that a separate calibration 

was carried out for each lens which was used on the Nikon D3X in Bhutan. Due to the 

fact that only the 50 mm lens was used for the terrestrial documentation of Drapham Dzong, 

only the results of this calibration process are shown here (table 9). The other lenses were only 

used for documentary pictures of the field campaign. 

30


Table 9: Calibration results of the Nikon D3X. 

Resolution, width 

6048 pixels 

Resolution, height 

4032 pixels 

Pixel width 

5.9 microns 

Pixel height 

5.9 microns 

Focal length 

52.4558 mm 

Coordinate xp, principal point 

0.0949 mm 

Coordinate yp, principal point 

-0.1822 mm 

K1 

5.2256e-005 

K2 -1.1876-008 

K3 

-1.1761e-011 

4.2 Preprocessing of the Terrestrial Images 

Since most of the work steps during the preprocessing of the terrestrial images were very similar 

to the steps of chapter 3.2 only the relevant facts and results are detained in the following. 

Image Selection 

During the terrestrial documentation of Drapham Dzong all in all 95 photographs were taken. 

As already mentioned in the introduction the data content of these images is the eastern part of 

the castle complex which is located at the bottom of the hill with the main ruin. This part of the 

excavation mainly consists of two ruin objects. The first is situated more to the west, directly 

next to the hill, and is slightly larger (figure 24). The smaller object represents the putative water 

mill of Drapham Dzong and lies more downhill (figure 25). Due to difficult topography on 

the site, the terrestrial photographs feature irregular recording positions. This issue complicates 

additionally the starting position for the data processing. 

Figure 24: Larger object of the additional ruin structures of Drapham Dzong. 

31


Figure 25: Putative water mill of Drapham Dzong. 

Due to the fact that the two objects mentioned above are spread widely apart and therefore the 

relevant images do not feature appropriate overlapping areas, it was decided to evaluate them 

separately. This fact fundamentally affected the terrestrial image selection. The final images 

which were selected for the photogrammetric processing can be divided into two groups. The 

first group describes the larger object closer to the hill and consists out of 14 pictures. This 

group can be additionally divided into four image strips whereupon each strip shows the building 

from different perspectives. The data content of the second group is the smaller object. This 

group contains seven images and cannot be divided into any further image strips (Grün, et al., 

2010). Figure 26 and 27 show the camera positions and the images which were finally used for 

the terrestrial image processing. 

Figure 26: Camera positions and the corresponding photographs of the first terrestrial image group. 

32


Figure 27: Camera positions and the corresponding photographs of the second terrestrial image group. 

Image Enhancement 

The image enhancement of the terrestrial photographs was performed using Photoshop. The 

enhancement values which were applied to the terrestrial images are listed in table 10. 

Table 10: Enhancement values (in Photoshop) of the terrestrial images. 

Contrast 

Brightness 

Strip 1 -25 -30 

Strip 2 -30 -35 

Strip 3 -25 -30 

Strip 4 -25 -30 

Caption of the Images 

The caption code for the terrestrial images is essentially the same as the one of the UAV images. 

The only two differences are: 

 

 

The names of the images logically do not feature any flight number. 

Since the terrestrial pictures were not rotated, only the brightness and contrast are listed as 

enhancement values. 

4.3 Orientation of the Terrestrial Images 

For the orientation of the terrestrial images, PhotoModeler 6 emerged as a convenient software. 

The image orientation procedure in PhotoModeler was similar to the one in LPS. However, 

there were some essential differences, of which the most significant was that PhotoModeler 

calculates the relative and the absolute orientation separately. Furthermore, as previously mentioned 

above the terrestrial images were processed in two groups. For this reason two separate 

projects were set up in PhotoModeler. In the following paragraphs, unless specifically mentioned, 

all procedures in the two projects were identical. 

The first step of the image processing in PhotoModeler was the import of the terrestrial images. 

Accordingly, the camera which was used to take the photographs had to be defined. Therefore 

the calibration values from iWitness were transferred into the project in PhotoModeler. With the 

33


exception of the principal point coordinates, all values were imported without any changes. The 

reason why the coordinates of the principle point needed to be adjusted manually was a different 

definition of the point of origin of the image coordinate system. While iWitness defines this 

point to be in the middle of an image, PhotoModeler considers the top left of a picture as the 

point of origin. The relation between the old and new coordinates of the principal point is shown 

in table 11. 

Table 11: Adjustment of the principal point’s coordinates (Nikon D3X camera). 

Old (iWitness) 

New (PhotoModeler) 

x-coordinate 0.0949 mm 18.0949 

y-coordinate -0.1822 mm 12.1822 

The next procedure was the manual measurement of tie points in the first two images. As in the 

UAV photographs, a good distribution of tie points in the overlapping area was also aimed for. 

This procedure was accomplished with PhotoModeler’s Mark Points Mode and Referencing 

Mode and also included the measuring of GCPs as tie points. Since the program numbers all 

measured points consecutively, it was not possible to apply a systematic point code here. In 

general the manual measuring of tie points in the terrestrial images was more difficult than in 

the UAV images. As there were not as many walls as on the top of the hill, stones and stumps 

were also used as tie points. Figure 28 shows a typical example of a manually measured tie 

point in PhotoModeler. 

Figure 28: Example of a manually measured tie point in PhotoModeler. 

Furthermore, it was not possible to generate enough well distributed tie points automatically in 

the terrestrial images. The reason for this were occlusions and the vegetation in the overlapping 

areas which caused significant differences in image texture. Figure 29 shows an example of 

automatic generated tie points in two images. Those were computed with the software Gummireifen 

by David Novák. It is apparent that this point distribution is suboptimal. In the end, 

approximately 20 tie points resulted out of the manual measurement (Grün, et al., 2010). 

34


Figure 29: Automatically generated tie points in two terrestrial images. 

After all tie points in the overlapping area were measured, the image pair was oriented relatively 

with the PhotoModeler Process function. In this procedure the remaining images without any tie 

points thus far were ignored and not oriented. After a successful orientation of the first two images 

both pictures were retained. This means that they were used for the following process, but 

the adjustment 11 of the images calculated above was not changed anymore. 

Now the third image was attached to the first two images and the manual measurement of tie 

points started from the beginning. Firstly all previously measured tie points which were visible 

in the new overlapping areas 12 were imported. Then some additional tie points were measured 

until approximately 20 well-distributed tie points were available in the new overlapping areas. 

11 The adjustment of the images means their relative orientation. 

12 One overlapping area accrues between the first and the third image and one overlapping area accrues 

between the second and the third image. 

35


Subsequently the third image was relatively oriented with respect to the already existing adjustment 

of the first two images. Likewise, after processing, the image was also retained and not 

adjusted in the following procedures anymore. This sequence was completed for all terrestrial 

images. Sometimes PhotoModeler was not able to calculate a relative image orientation. This 

was the case if the camera positions from which the photographs were taken were too close to 

each other and therefore built a short base. Since this problem reoccurred several times during 

the processing of the terrestrial images, further photographs were sifted out and not used in the 

orientation process. More details in context with the number of oriented pictures are listed in 

table 12. 

Finally, the absolute orientation of the terrestrial photographs was calculated. For this procedure 

the previously calculated UTM coordinates of the available GCPs were used. The absolute orientation 

was the only step in the image orientation which was fundamentally different for the 

two image groups. As four GCPs were visible in the pictures of the first group, it was possible 

to calculate the absolute orientation of those photographs directly. Thereby the coordinates of 

the GCPs were imported into the PhotoModeler project by using the software’s Imports Explorer. 

The coordinates of the control points were then allocated to the corresponding points in the 

PhotoModeler project, which so far served as tie points. Finally a new orientation run was accomplished 

in which the absolute orientation of the whole image block was determined. Due to 

the fact that no GCPs were visible in the pictures of the second image group, it was not possible 

to calculate directly an absolute orientation. Therefore the absolute orientation of the second 

image group was calculated in a roundabout way via eccentric control points. Eccentric control 

points are points, whose coordinates can be computed by using already oriented images. As 

parts of the smaller objects were also visible in the first image group, it was possible to calculate 

four eccentric control points. These points enabled the absolute orientation of the second image 

block. The correct absolute orientation of the first image group was the precondition for this 

procedure (Grün, et al., 2010). The quality of the final orientation results is shown in table 12. 

Table 12: Results of the image orientation of the terrestrial images. 

Image block 1 Image block 2 

Total image number 12 5 

Oriented images 8 5 

Overall RMS 0.78 pixels 0.529 pixels 

Maximum RMS 1.483 pixels 0.922 pixels 

Minimum RMS 0.001 pixels 0.012 pixels 

Maximum point tightness 0.064 m 0.0095 m 

Minimum point tightness 2.5e-005 m 0.0001 m 

Overall RMS vector length 0.0828 m 0.0238 m 

Maximum vector length 0.324 m 0.044 m 

Minimum vector length 0.0302 m 0.0132 m 

4.4 Modeling of the Recorded Area 

The first part of the modeling was accomplished in PhotoModeler 6 likewise. Therefore the 

final PhotoModeler file of the image orientation was split into three different projects. In the 

first one, points along the top edges of all walls were measured clockwise (figure 30). This proceeding 

was necessary with a view to the following modeling process in CCModeler 5.0. In 

order to get 3D coordinates of the measured spots, all points needed to be measured in at least 

two images. Since some walls were obscured by trees, it was not possible to detect all structures 

in very high detail. 

36


Figure 30: Points measured in a clockwise sense on the top edge of a ruin wall. 

In the second and third project of PhotoModeler further points on the bare ground were measured. 

In general it was aimed to measure spots in a raster pattern with a raster width of approximately 

1.5 m. Because of the dense vegetation on-site it was not possible to measure such points 

in each part of the recorded area. The spots measured in the second and third project represented 

the environment of the ruin structures and needed to be measured in at least two images as well. 

The third project in particular contained points along the bottom edges of the ruin walls. From 

all these points a DTM was generated by Martin Sauerbier using the software DTMZ which is 

an interpolation program based on the finite element interpolation. Since the DTM of the smaller 

ruin object did not cover the whole area with all ruin walls, some additional pseudo points 

were inserted manually into the concerning model. 

The second part of the modeling was the reconstruction of the whole 3D scene with the program 

CCModeler 5.0. Therefore the points of the first project and the previously computed DTM 

were imported into a new CCModeler file. For the reconstruction of the ruin structures the 

House reconstruction tool of the software was used. This procedure can be described in the 

following way: The points of the top wall edges were connected to a triangulated irregular network 

(TIN) which represents the upper surface of a ruin wall. The border of this surface was 

extruded vertically and intersected with the DTM. From this followed a water-proof 3D model 

of a ruin wall (Grün, et al., 2010). This process was applied repetitively to all walls. The result 

was a 3D model of the whole scene. 

After the reconstruction some walls featured deficits in geometry. To rectify these shortcomings 

these walls were edited using the software CC Edit 5.0. The tool which was applied in this connection 

was the Snap to 3D point function. Due to problems caused by the software, this work 

step was only applied to the model of the larger ruin object closer to the hill. 

4.5 Results 

The results of the terrestrial image processing are two 3D models of the ruin objects situated on 

the bottom of the hill (figure 31 and 32). In the current status only the model of the larger object 

(figure 31) is edited. The next step in a future work should be the editing of the second model. 

In particular that means to straighten certain walls and to eliminate blunders which followed 

from the modeling process. Moreover, in the terrestrial case there are no usable texture images 

available. The reason for it is the difficult topography on location in addition to fences which 

37


are placed directly in front of certain walls. Anyhow it was tried to furnish the model of the 

larger ruin object with textures. This step was done with the aid of Fabio Remondino by using 

the software TextureMapping3D. As expected, the resulting 3D model was unusable for the 

purpose of the visualization. Therefore, it was not used in the further procedures. An alternative 

solution for this problem is to use artificial textures. Also conceivable is to extract some usable 

textures from the available image data and to stitch them repetitively on the digital 3D model. In 

order to compare different software packages on their modeling capabilities, it would be also 

interesting to reconstruct the ruin objects with another program (e.g. a CAD software). 

Figure 31: 3D model of the bigger ruin building resulting from the terrestrial image processing. 

Figure 32: 3D model of the smaller ruin building resulting from the terrestrial image processing. 

38

Further Works 

5 Further Works 

The work which is mentioned in this chapter was mainly done for the purpose of the visualization 

of Drapham Dzong at the Bhutan exhibition at the Rietberg Museum in 2010. As already 

mentioned in the introduction, one part of the visualization is a flyover of the larger surrounding 

area of Drapham Dzong. The flyover implements both, a digital model of the ruin structures as 

well as a rough DSM of the surrounding area of the excavation. In order to be able to generate 

such a DSM the required image data was needed. Therefore, a stereo scene of the adequate area 

was acquired at the beginning of 2010. A brief description of the DSM generation can be found 

in chapter 5.2. The digital model of the ruin structures consists mainly of two parts: the model 

of the main castle complex on the hilltop and the ruin objects downhill which were recorded 

with terrestrial photographs. The modeling process of the main citadel is the topic of the following 

chapter. The ruin objects further downhill were modeled within the bounds of the bachelor’s 

thesis of Maroš Bláha. This procedure is described in chapter 4 of this report. A further paragraph 

of this chapter shows a site map which serves also for the purpose to visualize the whole 

castle complex. 

5.1 Digital Model of the Main Ruin Structures of Drapham Dzong 

The main ruin complex was modeled by Fabio Remondino at the Bruno Kessler Foundation in 

Trento (Italy). This modeling process was realized with the software 3D Studio Max. A snapshot 

of the final result is shown in figure 33. 

Figure 33: 3D model of the main ruin structures of Drapham Dzong. 

In its current status the model features some shortcomings in both, geometry and texture mapping. 

However, for the production of the flyover it is possible to avoid these through the choice 

of a clever flight path. Indeed, for the purpose of an accurate 3D model of Drapham Dzong it is 

aimed to correct these insufficiencies. As an alternative to the 3D model it is also conceivable to 

use a DSM overlaid with an orthoimage of the concerning area for production of the flyover. 

5.2 Larger Area DSM and Orthoimage 

In order to be able to visualize a larger surrounding area of Drapham Dzong a GeoEye-1 stereo 

scene was acquired in January 2010. Hence a DSM with a covering area of 10.7 x 10.7 km 2 was 

generated by Martin Sauerbier at ETH Zurich. For this procedure the SAT-PP software was 

39

Further Works 

used. The functionality and applied methods of SAT-PP are already described in chapter 3.4 of 

this report. Furthermore, it should be mentioned that the orientation of the stereo scene was 

available through Rational Polynomial Coefficients (RPC); these were supplied with the images 

by the German Aerospace Center (DLR). Based on the generated DSM, an orthoimage was 

produced using the PAN channel with a ground resolution of 0.41 m and the RGB channels 

(1.61 m resolution) of the GeoEye-1 stereo scene. The result was a virtual textured 3D model of 

the landscape which can be used for the flyover. Figure 34 shows an extract of the DSM generated 

by GeoEye-1 imagery (Grün, et al., 2010). 

Figure 34: View towards south on the surrounding area of Drapham Dzong. The arrow points on the ruin of 

the castle (Grün, et al., 2010). 

5.3 Site Map 

In addition to the two above described visualization products, a map of the archaeological site 

was produced by Peter Fux (figure 35). The numerous wall structures on the top of the hill were 

measured in the LPS Stereo Analyst. The final LPS project which resulted from the UAV image 

orientation was used for these measurements. The visualization of the site map was produced 

using the ArcGIS software package. This included also implementing the models of the ruin 

objects at the foot of the hill into the map (green area in figure 35). 

40

Further Works 

Figure 35: Map of the archaeological site at Drapham Dzong (Grün, et al., 2010). 

41

Conclusions 

6 Conclusions 

Based on the raw data which was acquired during the field campaign in November 2009, the 

photogrammetric visualization of the ruin of Drapham Dzong was accomplished within the 

framework of this project. Since the main citadel was recorded with aerial images while additional 

ruin objects were documented with terrestrial images, the starting position of the data 

processing was tricky. In order to achieve optimal results different photogrammetric methods 

and software packages were applied. Furthermore the UAV images and the terrestrial images 

were processed separately. One of the main difficulties in the UAV image processing was the 

DSM generation. Because of the complex ground level of Drapham Dzong area it was not possible 

to generate a faultless DSM. Therefore, an additional DTM was measured manually and 

used for the corresponding working steps. Also the processing of the terrestrial images included 

problems. In this procedure, the photographs itself were the biggest challenge. Due to difficult 

environmental conditions on site some of the images feature occlusions and adverse perspectives. 

The final results of this project are three orthoimages of the main castle complex and a 

digital 3D model of further ruin objects adjacent to the main citadel. Products which occurred in 

an expanded frame of this venture are a map of the archeological site, a digital model of the 

main ruin structures and a larger area DSM in addition to a further orthoimage. Moreover, the 

modeling of the main fortress was further continued. The outcomes of this proceeding were 

presented on the ISPRS congress 2012 in Melbourne (Grün, et al., 2012). 

In spite of the above mentioned difficulties during the data processing the achieved results have 

potential and can be used for further works and projects e.g. in archeology. In particular, the 

orthoimages are appropriate for the generation of a site map. Furthermore it is possible to continue 

processing the present results. Especially some parts of the digital 3D model but also the 

orthoimages feature shortcomings which can be rectified. Moreover, it is possible to generate 

new products from the raw data (e.g. a physical 3D model of the Drapham Dzong ruin complex). 

A couple of such products have already been realized. The results of these works are 

shown in chapter 5. 

42

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