THE DEVELOPMENT OF DORIS: AN OVERVIEW* Ahmed ...

THE DEVELOPMENT OF DORIS: AN OVERVIEW *
Ahmed Mohamed
Alberta Research Council Inc., 3608 33 St. N.W. Calgary, AB T2L 2A6
Tel: (403) 210-5329 Fax: (403) 210-5395 Email: mohamed@arc.ab.ca
Rob Price
Alberta Research Council Inc., 3608 33 St. N.W. Calgary, AB T2L 2A6
Tel: (403) 210-5328 Fax: (403) 210-5395 Email: price@arc.ab.ca
Dave McNabb
Alberta Research Council Inc., P.O. Bag 4000, Vegreville, Alberta T9C 1T4
Tel: (780) 632-8264 Fax: (780) 632-8379 Email: dhmcnabb@arc.ab.ca
Jim Green
Optech Inc., 100 Wildcat Road Toronto, Ontario, Canada M3J 2Z9
Tel: (416) 661-5904 Fax: (416) 661-4168 Email: jimg@optech.on.ca
Patrick Spence
Optech Inc., 100 Wildcat Road Toronto, Ontario, Canada M3J 2Z9
Tel: (416) 661-5904 Fax: (416) 661-4168 Email: patricks@optech.on.ca
KEY WORDS: Ortho-rectification, Laser scanning, GPS/INS integration.
ABSTRACT
DORIS is an airborne imaging system that combines a laser scanner digital elevation
model (DEM) with simultaneously captured digital images to produce a highly accurate
ortho-rectified planimetric image map. DORIS and the laser scanner DEM provide a
complete spatial base layer in a GIS system and can virtually serve all mapping
applications requiring very high resolution and accurate 3D spatial information.
In this paper, an overview of the DORIS system will be given. Preliminary results from
airborne tests will be presented to show the potential of the system in different
applications.
* rd
Presented at the 3 International Symposium on Mobile Mapping Technology, Cairo-Egypt, January 3-5
2001.

INTRODUCTION
A suite of Geomatics and Remote Sensing (GRS) technologies for forest resources has
been under development at Alberta Research Council for years [Pollock 1998a, Pollock
1998b]. The focus of the new technology is on acquiring data for fundamental
biophysical entities of sustainable forest eco-systems. Among the objectives of the GRS
initiative is the opportunities for reducing the cost of the planning and conduct of forest
operations [McNabb 2000a].
Technology drivers for change include many readily available and developed tools. In
the mapping area, GPS/INS, digital cameras, laser scanners, and GIS stand as crucial
tools for modern mapping systems. Many new decision support tools and spatial planning
models rely on this technology. In fact, the modern mapping technology can improve
competitiveness and compensate for extensive, relatively low productivity land-based
operations. For instance, high spatial resolution digital imagery provides significant
advantages over many traditional image sources. To be most useful for forestry users,
any imagery products must also be properly geo-referenced [Ackermann 1996,
Quackenbush et. al. 2000].
Management of Resources data is becoming increasingly complex because the objectives
are dynamic in space and time. For instance, scale integration in forest resources is
limited due to the limits imposed by the existing data. Traditional vegetation mapping
approaches, for instance, require extensive field reconnaissance, and normally involve
delineation of vegetation boundaries through interpretation of aerial photographs [Coulter
et. al. 2000]. The inventory evolution of the resources information starts with the
commodity and goes through some processes until the resource value is reached. Synergy
is then achieved from integration of sensors and biophysical entities. With a synergy of
tools, a platform can be built on which others can build additional applications, or is used
by other management systems.
The need for new tree inventory system stems from the fact that Canadian and Alberta
inventories are poor [McNabb et. al. 2000b]. The sampling strategy for the current system
is extensive and labor intensive. It is currently used for single purpose, wood supply. The
new technology help provide critical measure of sustainability of harvest levels. As
regards the landform and soil mapping, the need for new tools is driven by the fact that
the current maps are poor at best and most forests have not been mapped. During the
recent years, there has been increased interest in the increased value of water and other
forest-related resources. The GRS adds forest productivity assessment and management
tool which in turn maximizes potential for high resolution DEMs, predictive landform
and soil mapping, and adds intelligent systems and decision support approach.
The components of the GRS suites of technologies include aerial multi-sensor integration
for data acquisition, tree imaging and forest inventory, landform and soil mapping under
the umbrella of the integrated natural resources inventory and information system. The

driver for change is the need for high resolution, spatially accurate data. Scale integration
from tree to stand to landscape and the multi-resource applications and values add
improved forest inventory and growth forecast and site specific management [McNabb et.
al. 2000b]. The focus of this paper is on the development and testing of a high-resolution
spatially accurate data resource system called DORIS (Differential Ortho-Rectification
Imagery System).
SYSTEM DESCRIPTION
The DORIS system consists of a hardware component and software one. In the
following, a brief description will be given for both.
DORIS Hardware Component
The DORIS mapping system consists of an imaging component and a laser-scanning
component. Although other imaging sensors are possible, the currently used imaging
component of the DORIS system is a digital frame camera. The laser-scanning
component of the system is an Airborne Laser Terrain Mapping lidar system. Both
components are directly geo-referenced by a GPS/INS system on board of the platform.
Fig. (1) illustrates the system as installed onboard of the airplane, while Fig. (2) shows
the control unit of the system. The scanning-laser is an all-digital system that quickly and
economically collects terrain data at high sample densities. Intelligent processing of laser
data can classify the laser points as belonging to the terrain or belonging to objects near
the ground, like vegetation, buildings, power lines, or other structures, see [Gutelius et.
al. 1996] for more details. A Kodak Professional Digital Camera System is used in the
proto-type of the DORIS system as an imaging sensor. The specific model DCS 520c that
was used in the test flight is 3-color camera (RGB) with 12 bits/color and has a frame
size of 1728x1152 pixels, where the actual pixel size on the CCD is 13 µm. A customized
embedded Applanix POS/AV IMU and on-board NovAtel OEM3 Millennium GPS
receiver provided the direct geo-referencing information to the DORIS system.
Fig. (1): DORIS Hardware Fig. (2): DORIS Control Unit

DORIS Software Component
The software part of the DORIS system has two components, field and processing. The
field component consists of image acquisition (DORISia) software, imaging system
calibration (DORIScc) software, and a laser/camera fine alignment (DORISfa) procedure.
The processing component consists of software to provide laser DEM griding (gendem),
image differential rectification software (DR) and Mosaic construction software
(compose).
Image Data Acquisition
Images from a digital frame camera are
acquired using DORISia software. In real
time, the image frames are time-stamped
and geo-referenced by the INS/GPS
system. Time stamping involves a
hardware communication component
between the camera and the GPS receiver.
Geo-referencing, however, involves
processing the INS/GPS data, interpolating
information at exposure stations and
synchronizing the different data streams
inside of the DORIS system. Fig. (3)
illustrates the user interface of the
DORISia software.
Image System Calibration
Fig. (3): DORISia Software
For the imagery system to yield
meaningful information, intrinsic and
extrinsic parameters about the camera
system must be known. The intrinsic
camera parameters are its calibrated
focal length, the location of the image
principal point and the lens distortion
characteristics. The extrinsic
parameters, on the other hand, determine
the relationship between the camera
coordinate frame and the mapping
coordinate frame. While it is
recommended to calibrate the system in
Fig. (4): DORIScc Software
lab conditions to yield optimum
calibration results, it is possible to obtain an estimate of the calibration parameters inflight
with a well-distributed target field in a designated calibration area. For the DORIS
system, the in-flight calibration is done for every installation of the system, while the lab

calibration is to be carried-out every year for maintenance purposes. Fig. (4) illustrates
the user interface of the DORIScc software.
Laser/Camera Alignment
The ALTM laser system provides the elevation model required for rectifying the imagery
of the DORIS system. Because they are two distinct systems, the DORIS camera system
and the laser-scanning system cannot occupy the same point in space at the same time.
Therefore, the relationship between the camera coordinate system and the laser
coordinate system should be defined. The definition of the camera-to-laser coordinate
system relationship is done through a target field that is known/measured in both systems.
This procedure also involves the on-board INS/GPS system. There are also several
operational considerations that should be taken into account, for the tight coupling of the
two systems.
Laser DEM Girding
The geometric final output of the laser scanner system
is a DEM in a common coordinate system in the form
of (x,y,z) coordinates of the laser-scanned points. Since
the laser scans the area in a saw-tooth irregular form,
the resulted DEM has to be grided in a regular raster
format. This is purely a software task and involves
estimating the elevation of the grid points from the
neighboring laser-scanned points. The final raster DEM
coincides with the image raster and determine the
resolution of the final mosaic-map. Fig. (5) illustrates
the user interface of the GenDEM software.
Image Differential Rectification and Mosaicking
The acquired images by DORISia, timestamped
by GPS and geo-referenced by
INS/GPS, are used along with the
calibration information to compute the
ground coordinates for each pixel of the
non-rectified image. The ortho-rectification
process involves correcting for relief
displacement and platform disorientation
[Wolf and Dewitt, 2000].
A mosaic map with color balancing is then
constructed from the individual rectified
Fig. (5): DORIS GenDEM Software
Fig. (6): DORIS DR Software

images. Fig. (6) illustrates the user interface of the DR software.
DORIS Information Flow
The DORIS process starts with acquiring the camera and laser scanner data. After
processing the laser data, a non-raster digital terrain model (DEM) is acquired. A raster
DEM is then produced by griding the laser ground points. The camera data, on the other
hand, is corrected by the calibration parameters to produce calibrated non-differentially
rectified (NDR) images. Both the DEM and the NDR images along with the georeferencing
information are input to the image differential rectification and mosaic
module to first rectify the images and then produce the mosaic map.
FLIGHT TEST
A flight test was carried out in the
Greater Toronto area-Ontario, Canada
in April 1999 to test the DORIS prototype
system. The flight included a
calibration site and an operation one.
The calibration site was flown more
than once and in two perpendicular
directions, namely in a near east-west
direction and a near north-south one.
Two perpendicular directions were
specifically chosen to de-correlate
direction-related errors. Multiple
calibration lines of flight were flown to
strengthen the calibration/alignment
Fig. (7): Flight Trajectory over the
Calibration Site
procedure and allow for redundancy. Fig. (7) shows the trajectory of one of the flights
over the calibration site.
The calibration site included a building with a long straight side to calibrate and fine-tune
the laser-scanning system. The corners on top of the roof of the calibration building were
precisely surveyed using differential GPS. A number of ground control targets were also
surveyed using the same GPS technique. The ground targets were two-meter by twometer
canvases with black and white x-like symbol. The total number of building-corners
and ground control targets were about 40. The proto-type configuration and the average
flying height above ground for this specific test resulted in an average ground pixel size
of 0.30 meter.

ERROR BUDGET
Because the DORIS system has multi sensors,
the overall positioning accuracy of the system is
a result of a combination of error sources. Fig.
(8) illustrates the different coordinate frames
used in the DORIS system.
The error budget of the DORIS system is
affected by the following two main factors,
intrinsic parameters and extrinsic parameters.
The intrinsic parameters include camera lens
calibrated focal length, location of CCD
principal point, and lens distortion
characteristics. The extrinsic parameters, on the
other hand, include camera coordinate frame to
mapping coordinate frame alignment, camera
coordinate frame to laser (user) coordinate
frame alignment, camera system measurement
errors, laser system measurement errors and
GPS/INS geo-referencing system errors. The
error sources can be summarized as due to:
• Geo-referencing
• Pixel estimation
• Calibration and alignment.
The above different sources of errors can be
described in an equation form as follows:
X + S R r + ∆r
e = e + e + e
2
p
l l
p = X GPS /
INS
2
GPS / INS
2
i
c
l
c
c
2
CLA
Eq. (1)
Where:
First term: positioning error due to the direct georeferencing
system,
Second term: position estimation error in the
imaging system,
Last term: error due to calibration and alignment
process.
i
c
Imaging Frame
(Camera)
Object
Navigation Frame
(GPS)
User Frame
(Laser)
Fig. (8): DORIS System
Coordinate Frames
Body Frame
(INS)
The positioning error due to the direct geo-referencing system is most pronounced at the
edges of the image frames. This is partially due to the direct effect of the IMU on the
attitude information and due to its indirect effect on the laser DEM. The error due to the

imaging system is also most pronounced on the edges of the image frames due to mainly
the lens distortion characteristics. Compensation of lens distortion, incorrect principal
point estimation and imperfect focal length contribute make up most of the error. The
rectification estimation algorithm is also a contributing factor to that error. Error due to
mis-alignment between the camera system and the laser-scanning system is systematic in
nature and contribute a large value in the overall error budget.
TEST RESULTS
Table (1) shows horizontal errors by comparing the coordinates of the GPS-surveyed
control points to the computed coordinates of the same points on the images after being
calibrated and ortho-rectified. Because two different types of control points were used,
two sets of results are presented in the table.
Results from ground targets only show more consistency (better standard deviation) than
results from all the points (building corners and ground targets). The uncertainty in
defining the image and ground coordinates of the building corners is the obvious reason
for that. While it was easy to define and measure the ground targets, the shadow effect at
the building edges limited the ability to correctly define the corner points.
Considering the pixel size of 0.30 m for this flight test, the standard deviation of the
errors of 0.20 m is two-thirds of a pixel. In other words, within an image frame, errors of
less than 0.20 m are expected to happen 67% of the time. The concentration of this type
of error is most likely to happen around the middle of the image frame. Errors as large as
0.60 m (3σ) are expected to happen close to the edge of the image frame due to lens
distortion and geo-referencing problems close to the swath edge.
The bias in the error is due to two factors, long GPS baseline and misalignment. Because
the master GPS station happened to be more than 50 km away from the calibration site,
the residual atmospheric effect manifested itself as a bias in the final solution. Also,
residual misalignment between the camera system and the laser system shows the same
behavior. Adding the bias to the final results, the ‘rms’ error is approximately one and
two-thirds of the pixel size.
Table (1): Horizontal Position Errors
Points Error [m] E N |E| |N|
Mean 0.28 0.01 0.46 0.37
All
Points
Ground
Targets
Only
Max 0.80 0.80 0.80 0.80
Min -0.66 -0.62 0.01 0.03
Sd 0.44 0.44 0.22 0.23
Rms 0.52 0.44 0.51 0.44
Mean 0.46 -0.28 0.46 0.28
Max 0.59 -0.07 0.59 0.62
Min 0.06 -0.62 0.06 0.07
Sd 0.18 0.20 0.18 0.20
Rms 0.49 0.35 0.49 0.35

Fig. (9) shows a final DORIS image mosaic. Fig. (9a) shows a mosaic of a few image
frames on a UTM grid. Fig. (9b) shows a building corner using only the laser data; it can
be noticed how dense the laser points are to the limit that a building corner can be
identified. Fig. (9c) shows a mosaic seam horizontally across two image frames and the
produced color balancing. Fig. (9d) shows a ground target canvas and the corresponding
computed position of it on the mosaic map.
SUMMARY AND OUTLOOK
The aim of the suite of Geomatics and Remote Sensing (GRS) technologies for forest
resources is to reduce the cost of the planning and conduct of forest operations. The
initiative also includes building tools for tree inventory and better management of the
forest resources. . With a synergy of tools, a platform can be built on which others can
build additional applications, or is used by other management systems.
An airborne multi-sensor mapping system has been under development for years at
Alberta Research Council under the GRS initiative. DORIS combines a laser-scanning
technology with digital imaging technology to produce high-resolution and highly
accurate ortho-rectified planimetric image map. With the laser scanner DEM, DORIS
provides a complete spatial base layer in a GIS system and can virtually serve all
mapping applications requiring very high resolution and accurate 3D spatial information.
The DORIS system is all digital and highly automated because of its inherent structure. It
uses state-of-the-art direct geo-referencing provided by a highly accurate GPS/INS
system. Very fast turn-around time is possible with the DORIS system due to its digital
nature and high automation.
The overall error budget shows promising results of the system due to the highly accurate
hardware and software components and procedures. Test results show sub-pixel accuracy
(1σ) within the mosaic map. Systematic errors and biases due to atmospheric and
calibration effects contribute to the overall system accuracy. Errors due to mis-alignment
between the imaging system and the laser-scanning system contribute considerably to the
overall system accuracy.
The high-resolution capability of the DORIS system and its high accuracy and fast turnaround
time make it an ideal system for forest resources and other mapping applications
that require such capabilities. It also provides short wave length information that, when
combined with high-resolution satellite imagery, can provide a very economic and
feasible system.

SUMMARY
Normal text
Fig. (9a): ALTM raster Laser DEM
Fig. (9c): Seamless mosaic with color
balancing
Fig. (9d): Sub-pixel accuracy (image
compared to ground control
targets)
Fig. (9): DORIS Mosaic Map of the Calibration Site
Fig. (9a): DORIS Airborne mosaic air photo scale 1:25000 with
Kodak DCS 520 Professional Digital Camera

REFERENCES
Ackermann, Friedrich (1996). Airborne Laser Scanning for Elevation Models. GIM
Geomatics Info Magazine, Vol. 10, October 1996.
Coulter, Lloyd, Douglas Stow, Allen Hope, John O’Leary, Debbie Turner, Pauline
Longmire, Seth Peterson, John Kaiser (2000). Comparison of High Spatial Resolution
Imagery for Efficient Generation of GIS Vegetation Layers. Photogrammetry
Engineering and Remote Sensing Journal, Volume 66, Number 11, November 2000.
Gutelius, Bill, Donald Carswell, Jim Green (1996). Topographic Terrain Mapping
Using Scanning Airborne Laser Radar. GPS/LIS’96 Conference, Denver - Colorado,
Nov. 19-21 1996.
McNabb, Dave (2000a). Geomatics and Remote Sensing Program, Forest Resources
response to social, market, and technology changes in the Forestry Sector. FORES&T
2000 Workshop: Toward sustainability and improved competitiveness, Alberta Research
Council, Edmonton, Alberta, Canada, October 18-20, 2000.
McNabb, Dave, Ahmed Mohamed, Rob Price, Jim Green (2000b). High Quality Base
Maps for the Planning and Conduct of Forest Operations. GPS in Forestry:
Workshop 2000, The Grand Okanagan Lakefront Resort and Conference Center,
Kelowna, British Columbia, Canada, November 20 - 22, 2000.
Pollock, Richard (1998a). Overview of the Differential Rectification and Mosaic
Composition Software. Advanced Computing Business Unit, Alberta Research
Council, Calgary, Alberta, Canada.
Pollock, Richard (1998b). Development of Highly automated System for the Remote
Evaluation of Individual Tree Parameters. Advanced Computing Business Unit,
Alberta Research Council, Calgary, Alberta, Canada.
Quackenbush, Lindi J., Paul F. Hopkins, Gerald J. Kinn (2000). Developing Forestry
Products from High Resolution Digital Aerial Imagery. Photogrammetry Engineering
and Remote Sensing Journal, Volume 66, Number 11, November 2000.
Wolf, Paul R., Bon A. Dewitt (2000). Elements of Photogrammetry with Applications
in GIS. McGraw Hill, New York.