digital surface model generation from corona satellite images

Digital surface model generation from CORONA satellite images

Angela Altmaier *, Christoph Kany

Department of Geography, University of Bonn, Meckenheimer Allee 166, 53115 Bonn, Germany

Received September 2001; accepted March 2002

Abstract

Digital surface models (DSMs) are used for various analyses in environmental science, e.g. for erosion and water studies.

Aerial photos and maps, which are necessary for the extraction of DSMs, often do not exist due to financial or political reasons.

This situation can be also encountered in Morocco and, in particular, a test area of the international research project IMPETUS

was used in this study. Therefore, stereo satellite images of CORONA have been used, as they allow DSM generation, have a

ground resolution of 1.83 m, reasonable price (US$12–18 per filmstrip of 188� 14 km) and large coverage (especially of Asia

and eastern Europe). The software program ERDAS IMAGINE OrthoBASE Pro was used to generate DSMs automatically

from CORONA satellite images with best vertical accuracy of about 10 m and planimetric accuracy of about 3 m. These DSMs

could afterwards be used to generate orthoimages, e.g. for mapping change detection and generating thematic maps or land use

classifications. D 2002 Elsevier Science B.V. All rights reserved.

Keywords: CORONA; Digital surface models; DTM; Orthoimage; ERDAS IMAGINE OrthoBASE Pro; DGPS; Morocco

1. Introduction

For many tasks of environmental analysis and

environmental science, such as analysis of erosion

and runoff dynamics or vegetation and infrastructure

changes, digital surface models (DSMs) are an impor-

tant basis. The 3D view of a DSM can be used in

different software programs, e.g. generate anaglyph

images in ERDAS Stereo Analyst, to derive plani-

metric and 3D information for further use in change

detection analysis. An example might be the digitising

of slopes in the anaglyph image and comparing them

to older ones. On the other hand, orthoimages can be

generated from DSMs and once again be the basis for

2D change detection analysis or mapping. Facing this

challenge, temporal changes may be one of the most

important factors: information of DSMs and orthoim-

ages of different years can be compared, e.g. from the

actual IKONOS satellite images to the 30 years older

CORONA satellite images.

1.1. Research project and the need of DSMs

The mentioned possibilities of DSM use and their

integration into the actual research project IMPETUS

have been technical and conceptual motivations for

the following study. IMPETUS is the abbreviation for

‘‘Integratives Management-Projekt fur einen Effi-

zienten und Tragfahigen Umgang mit Sußwasser in

West-Afrika (an integrated approach to the efficient

management of scarce water resources in West

Africa)’’ (IMPETUS, 2001b). It is an interdisciplinary

and application-oriented project, with the final objec-

0924-2716/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.

PII: S0924 -2716 (02 )00046 -1

* Corresponding author. Tel.: +49-0228-2422008 and +49-089-

848151.

E-mail addresses: [email protected],

[email protected] (A. Altmaier).

www.elsevier.com/locate/isprsjprs

ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 221–235

tive of establishing a sustainable water management in

western Africa. Researches on conditions, influences

and realisation of this management are ongoing in two

different catchments in western Africa. One of the two

catchments is drained by the river Draa and situated in

southern Morocco. It extends from the southern slope

of the Atlas mountain chain to increasingly desertifi-

cated areas nearby the Moroccan–Algerian frontier

(Fig. 1). The objectives for initialising the IMPETUS

research project were the hypothesised interchanges

between the climates of Africa and Europe via tele-

connection processes as well as relations between the

long-lasting drought periods in the northern and

southern parts of the Sahara Desert. Therefore, the

hydrological cycle and the quantification of fresh

water resources are at the center of all research

studies. Especially, there is a great interest in differ-

entiating between climatic and socioeconomic influ-

ences on the hydrological cycle and the origins of

such influences.

For the research of almost all fields of this

IMPETUS project, DSMs will serve as a basis for

analysis, as mentioned in the Introduction. Hydro-

logical as well as geological, meteorological and

urban development researchers need DSMs as basis

for studies on dynamics of, e.g. erosion, runoff and

infrastructure. Depending on the special application

in the different research fields, DSMs or either

digital terrain models (DTMs) are more suitable.

Whereas DSMs include 3D objects, like vegetation

and buildings, DTMs describe only the earth surface.

The technical method of this study creates DSMs by

matching. However, due to the rare vegetation

coverage existing in Morocco (see below), the

DSM can be used as DTM in almost all parts of

the catchment area (except, e.g. the oases).

Fig. 1. River Draa catchment of the IMPETUS project in Morocco (in black) (IMPETUS, 2001a, p. 8).

A. Altmaier, C. Kany / ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 221–235222

1.2. New method of DSM generation

Maps and aerial photos have always been used as

basis for the generation of DSMs. Unfortunately, only

few such materials could be found in Morocco. Maps

exist only for a part of the research area in 1:100000

scale and dated from the 1960s. The main reasons for

this scarcity of material might be financial problems

and security restrictions. Nevertheless, more and more

activities concerning the geographical information

system (GIS) and remote sensing sector are currently

being developed in different Moroccan institutions

and organisations.

Against this background of low data availability, a

new method of DSM generation based on the high-

resolution CORONA satellite images offers a lot of

advantages. Their best ground resolution—as used in

this study—is 1.83 m. For other CORONA missions,

the ground resolution may vary between 1.83 and

140.3 m (see below). Since 1995, CORONA satellite

images are available at the US Geological Survey

(USGS, 2002) for the reasonable price of US$12–18

per filmstrip (188� 14 km), i.e. less than 1 cent per

square kilometre, though without digital preparation.

Comparing this price to the IKONOS satellite images

(about US$29 per square kilometre) or SPIN-2

(US$35 per square kilometre), it is evident that

CORONA gives a favourable financial possibility

for DSM generation in countries like Morocco.

However, it should be taken into consideration that

only few software programs like ERDAS IMAGINE

OrthoBASE Pro are able to generate DSMs based on

CORONA satellite images (see Section 3).

In the following study, the software program

OrthoBASE Pro was tested for DSM generation for

a part of the Draa river catchment in southern

Morocco. The results have then been evaluated con-

cerning accuracy and effort.

Fig. 2. Test area in Morocco (outlined) (RV-Verlag, 2000).

A. Altmaier, C. Kany / ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 221–235 223

The method of this case study should be applicable

for the whole catchment of the river Draa. Because of

the area of this catchment (estimated at ca. 30000

km2) and its extension up to the Atlas mountain chain,

the procedure of DSM generation must take into

account a large area with complicated geomorpholog-

ical features. Therefore, the driving goal was to

generate a DSM for an area as large as possible, with

the lowest possible financial and temporal effort (e.g.

only one DGPS field campaign, only a short but high

precision DGPS surveying, software processing time

of only some weeks).

1.3. Test area

For the first tests of DSM generation by Ortho-

BASE Pro, only a small part of the Draa catchment

was selected as a test area in this study. This test area

was situated at the southern slope of the Atlas moun-

tain chain, few kilometres north of the small village

Skoura. The test area’s size is about 100 km2 (Figs.

2–4). For DSM generation, the test area had to be

separated into two parts and a DSM of the northern

and southern part was generated. That was necessary

because the aft CORONA (see Section 2.1 for tech-

Fig. 3. View of the test area in the northern direction.

Fig. 4. View of the northwestern part of the test area.


nical specifications of CORONA) image of the test

area was also separated into these two parts on two

different adjacent filmstrips. After generation of these

two DSMs, a mosaicking of the two DSMs and

orthoimages in ERDAS was performed.

From a hydrological perspective, the test area

belongs to a region with water resource levels below

the average. Therefore, the typical Mediterranean

vegetation in the test area is only existent in very

few areas or even absent. The oases of the river Draa

and its small adjacent rivers are sometimes the only

major vegetation patches. Along these rivers, often

only some metres wide, typical small Moroccan vil-

lages of clay construction can be found. This low

density of settlement and the scarcity of vegetation

became a great advantage for the DGPS surveying. In

addition, these two aspects and a lack of erosion led to

almost no negative influences on the accuracy of DSM

generation in most parts of the test area, i.e. the DSM

was very close to a DTM and multitemporal differ-

ences due to erosion were small (IMPETUS, 2001a).

2. CORONA satellite images and DGPS surveying

2.1. CORONA satellite images

As the data basis for DSM generation, CORONA

satellite images were used. The CORONA satellite was

the first generation of the US reconnaissance satellites

built by the US Air Force and the US Central Intelli-

gence Agency (CIA). The CORONA satellite took

stereo photos from 1960 to 1972 using filmstrips of

7� 90 cm produced by a panoramic camera with

panchromatic channel (Day et al., 1998; Ruffner,

1995; Mc Donald, 1995a,b; Tappan et al., 2000) (Fig.

5, Table 1). The best ground resolution is 1.83 m for the

KH-4B mission at a flight height of 150 km (USGS,

2002). Two oblique viewing cameras realised the stereo

view, a forward- and an aft-looking camera each with a

15j tilt, i.e. an angle of 30j between them (Fig. 6).

Because of the panoramic camera, the filmstrips

have typical panoramic distortion (Slama, 1980;

Goossens et al., 2001; Fig. 5), which has to be

corrected after scanning by a mathematical algorithm.

Only few image and camera data specifications are

known for CORONA, as shown in Table 1 below. The

parameters ‘fiducial marks’ and ‘principal point’,

which are typically used for aerial photo triangulation,

do not exist. Therefore, triangulation and geometric

correction become very difficult (see Section 3).

Due to the military intention to spy on the former

Soviet Union and other sensitive areas, the principal

coverage areas of CORONA images are Asia, eastern

Europe and northern Africa. In 1995, the available

CORONA images of 2 billion square kilometres were

released to the public by the US government and can

be ordered from the USGS (USGS, 2002).

Depending on the CORONA mission, image qual-

ity, e.g. film and ground resolution or cloud coverage,

differs. The best film resolution varies between 160 L/

mm for KH-4B and 30 L/mm for KH-5, and the best

ground resolution is between 1.83 and 140.3 m.

However, cloud coverage prevents 40% of the images

from the full use (USGS, 2002).

In this study, CORONA satellite images of the very

successful last KH-4B mission with the ground reso-

lution of 1.83 m were used. The adjacent filmstrips

have an overlap of 10% in north–southern flight

direction. Therefore, a complete cover of the over-

flown area and a technical processing of the adjacent

images are made possible. In this study, the aft image

consists of two parts from two adjacent aft filmstrips

(about 30% for the northern part and 70% for theFig. 5. Film distortion of a panoramic camera (Slama, 1980, p. 201).

Table 1

Parameters of CORONA satellite for KH-4B system (USGS, 2002)

System, mission CORONA KH-4B, mission 1117

Date May 1972

Camera type panoramic, panchromatic

Frame format 5.54� 75.69 cm

Size of the imaged area 14� 188 km

Focal length 60.69 cm

Best film resolution 160 L/mm

Best ground resolution 1.83 m

Flight height 150 km

Image scale 1:247500


southern part of the test area). The forward image can

be found on one filmstrip.

Parts of the forward and aft CORONA filmstrips

showing the test area were scanned by a desktop

publishing (DTP) scanner, the DIN A3 flat bed colour

scanner EPSON 1640 XL with double CCD linear

sensors. The optical geometric resolution was 1600

dpi (ca. 16 Am), the radiometric resolution is 14 bits

per pixel and colour channel (internal and external)

and the optical density is 3.6. Leachtenauer et al.

(1998) mention that to get the CORONA film quality,

a scanning pixel size of 4 Am should be used, but this

was not possible with the used scanner and would lead

to very large data sets. The scanning pixel size

corresponds to a pixel footprint of about 4 m. Because

of the mentioned separation of the test area on two aft

filmstrips (north and south), two scans were necessary

for the aft images.

2.2. DGPS surveying

The second database for DSM generation was a

GPS surveying using a Differential GPS (Leica 300

System) in March 2001 in the test area in Morocco.

Fig. 6. Camera model of CORONA satellite (NRO, 2002; Campbell, 1996).


These DGPS-measured data were used afterwards

to evaluate the height accuracy of the software-gen-

erated DSM. Therefore, foot and upper slope positions

were measured, mainly on foot in a point distance of

10–20 m. On the other hand, points were measured

by DGPS to be used as ground control points (GCPs)

in the software OrthoBASE Pro during exterior ori-

entation. These GCPs were mostly measured by

vehicle in a point distance of about 50 m (Fig. 7).

Due to the DGPS, the accuracy of all points (in x, y

and z direction) is less than 10 cm. In this context, the

rare vegetation distribution was an advantage for

DGPS measuring. Even by vehicle, the basis for

surveying was always the ground, guaranteeing the

small deviation of DGPS measurements from the

ground. This precise point accuracy was needed to

improve the interior orientation, as shown below. The

time needed for the field campaign was 14 days.

For the measurement of the points, well-defined

positions corresponding to the image and the terrain

had to be found. However, the point collection was not

always easy due to the changing infrastructure (roads

and buildings) since the acquisition of the 30-year-old

CORONA images. These points had to be measured

evenly in all parts and height levels of the test area to

get precise results from OrthoBASE Pro triangulation

afterwards. The best positions could be found on the

crossings of roads and rivers. The last one is approx-

imately stable as well because of the arid climate in

Morocco. Still, this way of point collection was diffi-

Fig. 7. Points of DGPS surveying in the test area.


cult because of the few existing roads, the complex

terrain structure and the limited DGPS surveying

period. Therefore, it was impossible to measure GCPs

and slope positions in all parts of the test area. In total,

about 5000 points were collected by DGPS. Out of

these GCPs, again, only a few were valid and good

enough for precise triangulation results in OrthoBASE

Pro (see below). Therefore, just a few GCPs were

selected concerning their quality of corresponding

image and ground position as well as their regular

vertical and horizontal distribution. Due to lack of

experience with using Pro, the time needed for selec-

tion and measurement of good GCPs was about 4

weeks, but now, this task could be completed in 2–3

days.

3. Generation and applications of DSM

3.1. Triangulation and DSM generation

Usually, the interior orientation is defined by the

help of—among others—the image parameters ‘prin-

cipal point’ and ‘fiducial marks.’ However, from the

CORONA camera, these parameters do not exist.

Furthermore, CORONA has a panoramic camera,

which produces images with high geometric deforma-

tions (Fig. 5). Therefore, usually many steps are

required to correct the image and calculate the interior

orientation by removing the panoramic deformation by

a mathematical algorithm, and then allocating a pseudo

principal point and/or fiducial marks (relative to the

ones of the whole filmstrip) to each forward- and aft-

scanned image part. In this study, this way of calcu-

lation was not applied because (1) the two-step proce-

dure for CORONA is very complicated, time-intensive

and insecure (not scientifically verified), especially

considering the mentioned aim ‘large area coverage,

the least financial and temporal efforts possible’ and,

therefore, (2) the possibilities of the software Ortho-

BASE Pro regarding orientation should be evaluated.

OrthoBASE Pro demands as minimum input the

focal length, flight height and the pixel size of the

scanned image. The principal point is automatically

set to zero (i.e. at the image center) by the software.

The program treats the scanned image part as the

whole filmstrip, thus shifting the principal point from

its original position in the whole filmstrip. That would

not be problematic if the scanned forward and aft

image parts were exactly of same size and at the same

position on the filmstrips. However, this is almost

impossible, as the test area is separated on two aft

filmstrips. Scanning of the whole filmstrip would be

impossible as well due to the resulting large file size.

Nevertheless, it may be assumed that the impact of

this definition of the principal point on the object

point accuracy can be almost neglected due to (1) the

great flight height and the narrow bundle of the

projection rays and mainly because (2) this effect is

absorbed by the exterior orientation parameters. Fur-

thermore, the accuracy of object coordinates is

improved by using more than the usually required

three GCPs in absolute orientation (see below). It

must be emphasised that the calculation of the interior

orientation of the CORONA images is a very critical

but interesting research topic and should be further

examined and improved in further studies. This study

is an empirical work based on certain data and tools

and the mentioned aim ‘large area coverage, the least

financial and temporal efforts possible.’ It shows that

the calculation of orientation is possible with some

good results but it also discovers weak points, which

should be further studied.

The interior orientation is followed by the exterior

orientation, divided into the relative orientation of the

forward and aft images and their absolute orientation

to the ground (Heipke, 1997; Mikhail et al., 2001; Fig.

8). The relative orientation establishes the relation

between the forward and aft image by using tie points.

OrthoBASE Pro automatically measures tie points in

both images. In the northern part of the test area,

OrthoBASE Pro generates about 200 tie points and in

the southern part about 400, in both cases almost

evenly distributed.

The absolute orientation in OrthoBASE Pro needs

either approximate exterior orientation parameters,

ephemeris information for the satellite images and

two measured tie points for each pair or three GCPs

for each image. To determine the approximate per-

spective center and rotation angles of each image,

either the similarity transformation, the simplified

space resection or the ephemeris transformation is

used (Wang et al., 2000).

The automatic tie point collection is realised in

OrthoBASE Pro by a structural image matching based

on the correspondence between two structural descrip-


tions. A structural description of an image consists of

a set of image features (points, lines and regions and

their attributes) and the topological and geometrical

relationships among the features. In order to improve

accuracy and speed of structural matching, procedures

like evaluation function, search method, rotation-

invariant cross-correlation method, correctness check

and extraction of structural descriptions are added. In

combining all these tools, the matching is fully

automated and realised without any a priori informa-

tion (Wang, 1998).

After the measurement of tie points, the triangu-

lation procedure was carried out. Three triangulation

procedures are available: self-calibration (if the cam-

era interior parameters are unknown) which was used

in this study (employing in this case the 12 additional

parameter model of Ebner), relative triangulation (if

there are no GCPs) or the triangulation based on

exterior orientation parameters and GPS/INS data

(Wang et al., 2000). The orientation model in the

form of self-calibrating direct linear transformation

(SDLT) does not require any sensor parameters like

the interior orientation and approximate exterior ori-

entation parameters (incident angle and ephemeris

information), and it also does not require any geo-

metric precorrection of the original image data (Wang,

1999).

The absolute orientation is usually calculated by

the input of at least two full GCPs and one height

GCP. As discussed above, more GCPs than usual were

Fig. 8. Measurement of GCPs and tie points in OrthoBASE Pro in three different zoom images.


necessary for this study: seven for the northern part of

the test area and six for the southern part (Fig. 9).

They were empirically selected to get the best possible

triangulation results, considering the exact corre-

sponding ground and image position of the GCPs as

well as a regular horizontal and vertical distribution of

the GCPs in the test area. A higher number and good

distribution of GCPs were necessary to improve the

complicated and approximate interior orientation of

the CORONA images. If there was an optimal interior

orientation, perhaps three required GCPs would be

enough, maybe even for the very large area of the

whole Draa catchment.

The same matching as for the tie point generation

was used in OrthoBASE Pro—after triangulation—to

create about 50000–70000 irregularly distributed

points with an average point distance of 20m for further

DSM interpolation. The area covered by the DSM was

about 100 km2. The point file and its contours were

created in ERDAS as well as in ESRI shapefile and

ASCII compatible format. The DSM interpolation was

possible in ERDAS IMAGINEVisualGIS, DataPrep or

ArcView 3D Analyst (Figs. 10–12).

The evaluation of triangulation and DSM accuracy

gave the following results. First, the accuracy of

triangulation is shown in OrthoBASE Pro by the

Fig. 9. Position of used GCPs (marked with a flag) in the northern and southern part overlaid on the calculated CORONA orthoimages (dark

regions are palm oases).


automatically calculated residuals (Table 2). The a

posteriori standard error from the triangulation adjust-

ment was 0.58 pixels in the northern part and 1.19

pixels in the southern part.

In addition, the absolute accuracy of elevation was

estimated by comparing DGPS-measured points with

the DSM points. These DGPS points belong to the

5000 DGPS points collected during the field cam-

paign in Morocco. In ArcView, the DSM points were

overlaid with the DGPS points, enabling the manual

calculation of the height difference of planimetrically

similarly situated points. For this comparison, it was

important to select points from almost all horizontal

and vertical parts of the image. As the DGPS survey-

ing was not as dense as the DSM points, a comparison

of 113 points for the northern part and of 70 for the

southern part of the test area was possible. The

following accuracy of elevation was calculated for

the northern and southern parts (Table 3).

The accuracy varies depending on the position of

the GCPs used for exterior orientation. Higher eleva-

tion differences between DGPS-measured points and

DSM points could be noticed in areas far from the

GCPs. The elevation accuracy of the southern part

DSM is a bit lower than that of the northern part

DSM. One reason for this is surely the less adequate

Fig. 10. DSM generated by OrthoBASE Pro interpolated in ERDAS VisualGIS: northern part above, mosaic of the two DSMs below.


GCP distribution in the southern part. This influence

of number of GCPs and their distribution may be

partly due to errors and approximations in the interior

orientation calculation.

3.2. Dependence of triangulation and DSM accuracy

In total, it can be assumed that triangulation and

DSM accuracy depend mainly on the following fac-

tors: camera model, film deformations, geometric

accuracy and resolution of scanner as well as accuracy

of interior orientation. Furthermore, the number, accu-

racy and regular horizontal and vertical distribution of

the GCPs are important, as also influenced by the

approximative interior orientation in this study. There-

fore, a very high accuracy (only a few metres) of the

corresponding ground and image position of GCPs

was attempted during DGPS surveying and image

measurement. The image position of GCPs has to be

marked in the CORONA images (scan print or photo

print). Finding the exact corresponding ground and

image positions during the field campaign was some-

times quite difficult because of scarce roads, inacces-

sible terrain or structural changes since the CORONA

image acquisition. Achieving the above GCP accuracy

requirement, and the good triangulation and DSM

results shown above without a perfect interior orienta-

tion, was quite an effort in this study and would be a

greater challenge for large areas, like the whole Draa

catchment. If the GCP measurement and triangulation

require less effort, when an accurate interior orienta-

tion is calculated, has to be checked in another study.

3.3. Examples of DSM applications: orthoimage and

anaglyph image

The OrthoBASE Pro-generated DSMs can be the

basis of further applications. It can be useful for 2D

and 3D applications, e.g. anaglyph images and ortho-

images.

Fig. 11. Northern part of the DSM with contours generated in OrthoBASE Pro, interpolated and visualised in ERDAS IMAGINE VisualGIS.


With the help of ERDAS Stereo Analyst, stereo

anaglyph images were created and the file with DSM

points was imported. A 3D visual control of the DSM

point positions was possible (especially in comparison

with roads, etc.). It is possible to continue processing

the DSM data in the anaglyph images by digitising,

e.g. slope or erosion features. These new data can be

used in a 2D and 3D comparison with older or recent

information to perform a change detection analysis.

Another application for DSM is the generation of

orthoimages. The DSM points created by OrthoBASE

Pro were used to generate the corresponding orthoim-

ages of the northern and southern parts. Afterwards,

the orthoimages of the test area were overlaid in

ArcView with the DGPS-measured points. The plani-

metric accuracy of the orthoimages (in x and y

direction) was calculated manually, as for the DSM

accuracy mentioned above. A comparison of 120

DGPS points for the northern and southern parts

relative to the orthoimages was made (Table 4).

For the northern part, a very high accuracy could

be achieved. Based on a pixel footprint of the scanned

images and an orthoimage pixel size of 4� 4 m, the

RMS accuracy of the orthoimage in relation to the

DGPS measured points is 1.8 m in x direction and 2.8

m in y direction (Fig. 13). For the southern part, the

accuracy is lower because of the lower DSM and

triangulation accuracy (RMS is 13.9 m in x direction

Fig. 12. Northern part of the DSM with elevation ranges generated in OrthoBASE Pro, interpolated and visualised in ArcView 3D Analyst.

Table 2

Accuracy of triangulation shown as statistics of the absolute

residuals of tie points (in m)

Northern part Southern part

x y z x y z

Average 2.5 2.7 12.5 4.8 5.7 21.6

Minimum 2.0 2.1 8.4 3.8 4.0 14.2

Maximum 4.1 4.4 22.3 7.0 9.7 38.1

Table 3

Accuracy of DSM elevation relative to DGPS-measured points

(in m)

Statistic Northern part Southern part

Average with sign � 2.6 2.4

Average absolute 7.7 10.7

RMS 9.7 13.3

Minimum � 24.5 � 34.3

Maximum 21.5 33.3


and 13.7 m in y direction). In addition, there is a large

systematic error (bias) in the y direction, which the

authors could not explain. The planimetric (radial)

RMS for the northern and southern parts was 3.3 and

19.5 m, respectively, the former result being within

expectations (error is less than 1 orthoimage pixel).

These orthoimages can be used in further studies

for change detection analysis, e.g. a comparison of

urban and settlement development, changes of vege-

tation distribution or changes of water resources like

reservoirs. Therefore, the 30-year-old CORONAortho-

images can be compared to other satellite images (e.g.

Landsat, SPOT and IKONOS) or aerial photos, if

available.

4. Conclusions and outlook

This study has been one of the first tests to apply

methods of DSM generation on CORONA satellite

images.

The processing for generating DSMs and digital

orthoimages from CORONA satellite images was

developed in an empirical way. Results of DSM and

orthoimage evaluation show a best accuracy of about

10 m in height and 3 m in planimetry. Due to the

complicated characteristics of CORONA images, the

software program ERDAS IMAGINE OrthoBASE

Pro was used, requiring only few initial orientation

parameters and no geometric precorrection of the

image data. Thereby, errors and approximations made

in the interior orientation, as well as possible geo-

metric scanner errors and not ideal GCP image meas-

Table 4

Planimetric accuracy of CORONA orthoimages, relative to DGPS-

measured points (in m)

Difference statistic Northern part Southern part

x y x y

Average with sign 0.3 � 0.5 1.5 10.1

Average absolute 1.5 1.3 10.9 10.8

RMS 1.8 2.8 13.9 13.7

Minimum � 4 � 5 � 12 � 12

Maximum 4 4 36 35

Fig. 13. CORONA orthoimages generated in OrthoBASE Pro, overlaid by DGPS measured points: section of the northern part left, and of the

southern part right.


urement accuracy, and GCP number and distribution,

may have contributed in a deterioration of the object

point positioning accuracy.

Nevertheless, it would be an interesting effort to

verify these results in further studies, including pre-

processing of the CORONA satellite images concern-

ing panoramic distortion, scanning process and interior

orientation calculation, as well as possibly using

CORONA images over areas with good reference

DTM, and easy to find and measure GCPs in order

to examine the geometric accuracy potential of this

imagery and possibly compare various sensor models.

Regarding the aim ‘large area coverage, the least

financial and temporal efforts possible’ for countries

like Morocco, the work was no minor effort and it

would be a great challenge to apply this method to the

entire Draa catchment area of about 30000 km2. It

would be interesting to examine if financial and

temporal costs are reduced when applying alternative

methods for the processing of CORONA data.

Acknowledgements

The authors would like to thank all institutions,

persons and companies who participated in this study.

This project had been supported by the Federal

Ministry of Education and Research (project number

07GWK02) and the State Ministry of Education,

Science and Research (project number 514-21200200),

especially Prof. Gunter Menz and Dipl. Geogr.

Michael Schmidt (University of Bonn, Remote

Sensing Research Group). Special thanks to the

company GEOSYSTEMS, Germering for providing

the software ERDAS OrthoBASE Pro as well as the

TU Munich (Mr. Czaja), the company Leica, Munich

and Dusseldorf, Mr. Fuhlbrugge (University of Bonn,

Department of Geodesy) and the surveying company

Kany, Goldkronach for lending GPS devices and

technical advice.

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