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Imaging, Photogrammetry, Surveying and GPS
- GIS Data Collection for the 21st Century
By John W Allan, Leica Geosystems GIS & Mapping Division, UK (Posted June, 2002)
As GIS becomes a standard management tool throughout many
organisations, questions are beginning to be asked about the
creation, maintenance and quality of the data that resides in the
database. This paper looks at the various methods of GIS
database creation with an emphasis on new update and
maintenance techniques from the surveying world. It also
addresses the use of new imagery data sources for data capture
and discusses new capabilities for managing and updating the
quality of existing databases.
 Data or Information? -
The information in the database goes through 4 distinct phases as
shown in Figure 1. Not only does the data have to be created in the
first place, (from which the information is gathered), but there is
also the issue of updating and maintaining the information.
Updating and maintenance can be carried out using a variety of
methods, depending upon the scale of the data and its usage.
Some of the methods are covered in later sections of this paper.
However, it is important to understand exactly what is meant by
updating.  Is it to improve the initial quality of the digital data or is it
to add new information that was either previously unavailable or
wasn’t obvious from the initial source? The former is covered under
the Quality Control section of the paper. The latter however can be
addressed by many techniques.
For large coverage areas, imagery from satellites or high altitude
cameras can be used. This covers a large area but only contains
simple information. At the opposite end of the scale, where
complex, “subjective” information is required, it is GPS that
provides the tool and the operator that provides the human
decision making process that results in the “complexity” of
information. The coverage in this instance is normally linked to a
single “point” or object, representative of the GPS’ accuracy. In
between these extremes lie the aerial sources of data that can be
captured at many scales to suit the application. Obviously, the
scale of the imagery defines the level of information that can be
captured. The other two phases from the diagram,
analysis/visualization and application/information usage depend
very much on the software application being used and as such will
not be covered in this paper in any detail.
Acquisition & Creation of GIS databases from imagery
The era of 1-meter satellite imagery presents new and exciting
opportunities for users of spatial data. With satellites from Space
Imaging (IKONOS), ImageSat (EROS) and DigitalGlobe Inc
(QuickBird) already in orbit, capturing imagery at up to 61 cm
resolution, high resolution imagery will add an entirely new level of
geographic knowledge and detail to the intelligent maps and GIS
databases that we create from imagery.
Some of the latest developments also include digital airborne
sensors. These new devices can be thought of as “airborne
satellites”, utilizing the same digital imagery capture systems as
satellites but offering the flexibility of capture of aerial systems
(See Figure 3). These new sensors will capture multispectral data
at extremely high rates and at resolutions of the operators
choosing. When linked with data from airborne Lidar systems,
which can give centimeter accuracy DTMs, they will provide the
basis for highly accurate but low cost base mapping anywhere in
the world.
Is high-resolution imagery making a difference?
There is no doubt that the GIS press has been deluged with high-
resolution imagery for the last 12 months. Showing an application
with an imagery backdrop provides an immediate visual cue for
readers. Without the imagery backdrop, the context is lost and the
basic map, comprising polygons, lines and points becomes more
difficult for the layman to interpret. It is the context or visual clues
that provide the useful information and it is this information that is
the inherent value of the imagery.
The higher the resolution of the imagery, the more man made
objects that can be identified. The human eye - the best image
processor of all - can quickly detect and identify these objects. If
the application is therefore one that just requires an operator to
identify objects and manually add them into the GIS database, then
the imagery is making a positive difference. It is adding a new data
source for the GIS Manager to use.
However, if the imagery requires information to be extracted from it
in an automated and semi automated fashion (for example, a land
cover classification), it is a different matter. If the same techniques
that were developed for earlier lower resolution satellite imagery
are used on the high-resolution imagery, (such as maximum
likelihood classification), the results can actually create a negative
impact. Whilst lower resolution imagery isn’t affected greatly by
artifacts such as shadows, high-resolution data can be. Lower
resolution data also “smoothes” out variations across ranges of
individual pixels, allowing statistical processing to create effective
land cover maps. Higher resolution data doesn’t do this - individual
pixels can represent individual objects like manhole covers,
puddles and bushes - and contiguous pixels in an image can vary
dramatically, creating very mixed or “confused” classification
results. There is also the issue of linear feature extraction. Lines of
communication on a lower resolution image (such as roads) can be
identified and extracted as a single line. However, on a high-
resolution image, a road comprises the road markings, the road
itself, the kerb (and its shadow) and the pavement (or sidewalk). A
very different method of feature extraction is therefore needed.
Figure 4 shows the range and variety of information contained in a
high-resolution image and the problems caused by shadows,
overhanging trees and parked cars.
It’s not just the spatial resolution that can affect the usage of the
imagery. With 11 bit imagery becoming available, the ability of the
GIS to work with high spectral content imagery becomes key. 11
bit data means that up to 2048 levels of grey can be stored and
viewed. If the software being used to view the imagery assumes it
is 8 bit (256 levels), then it will either a) display only the information
below the 255 level (creating either a black or very poor image) or
b) try to compress the 2048 levels into 256, also reducing the
quality of the displayed image considerably. Having 2048 levels
allows more information in shadowy areas to be extracted as well
as enabling more precise spectral signatures to be defined to aid in
feature identification. However, without the correct software, this
added “bonus” can easily turn into a problem.
One other area that needs to be addressed in terms of usage is the
actual availability of data to the end user. Application papers tend
only show us the finished results without giving any indication of
the actual project itself and the problems that may have been
encountered in the actual running of the project. In many
instances, availability of data is limited, especially from spaceborne
sensors and users have to look elsewhere for data.
An increasingly common source of image data is therefore existing
aerial survey photographs. With the massive improvement in
scanning technology and orthophoto production software, these old
photo archives can be readily made available to GIS users. No
licensing fees are required (as the organization generally owns the
photography) and the data can easily be made available internally
within the organization. The only downside is the question of how
recent the imagery is. Contrast this with the high-resolution satellite
data. If it is not archived data, then the data has to be acquired,
which is dependent upon both the weather and other demands on
the satellite. If it is acquired then it has to be processed and
shipped out via tape or CD/DVD (as bandwidth is limited) and
finally, it usage is limited by licensing - single user, multiple user,
site usage etc. pricing is therefore a key issue. The message here
is clear. High-resolution satellite data will not replace other sources
of data - it will in fact only complement them.
Finally, the issue of digital versus analog is also being addressed
in this new digital age. Old airphotos need to be scanned to
convert them to a digital format. New digital airborne cameras get
around this step, providing high quality airborne imagery at any
user defined resolution. Depending upon the application and the
levels of accuracy needed, cameras ranging in price from the
hundreds to the millions of dollars can be used. The drop in price
and increased availability of GPS units is also aiding the growth in
the use of low cost digital cameras for GIS applications. Attached
to remotely controlled aircraft or helicopters, they can provide very
high-resolution, targeted aerial surveys for specific applications.
Information (and its extraction) is the key element
As mentioned above, high-resolution imagery from both aerial and
space borne sensors provides a challenge to the user community
in terms of information extraction. The human eye and brain can
identify objects in the image but the computer finds it difficult. If we
cannot automate this process, then we will most certainly lose out
on some of the major economic benefits of the imagery.
If the human brain can do it, why can’t the computer? Well it
actually can if it uses rules or knowledge based processing, just as
the human brain does. The brain can make a decision on an image
very quickly by understanding and using context. If we see
grassland in the center of an urban development, we can easily
decide that it is a park, as opposed to agricultural land. To make
this decision we are using knowledge and experience to create
expertise and computer based expert systems are beginning to
emerge that mimic this process.
For many years, expert systems have been used successfully for
medical diagnoses and various information technology (IT)
applications but only recently have they been applied successfully
to GIS applications.
Statistical image processing routines, such as maximum likelihood
and ISODATA classifiers, work extremely well at performing pixel-
by-pixel analyses of images to identify land-cover types by
common spectral signature. Expert-system technology takes the
classification concept a giant step further by analyzing and
identifying features based on spatial relationships with other
features and their context within an image.
Expert systems contain sets of decision rules that examine spatial
relationships and image context. These rules are structured like
tree branches with questions, conditions and hypotheses that must
be answered or satisfied. Each answer directs the analysis down a
different branch to another set of questions.
The beauty of an expert system is that because the rules, also
called a knowledge base, are created by true experts (such as
foresters or geologists), the system can be used successfully by
non-experts.
In terms of satellite images, the knowledge base identifies features
by applying questions and hypotheses that examine pixel values,
relationships with other features and spatial conditions, such as
altitude, slope, aspect and shape. Most importantly, the knowledge
base can accept inputs of multiple data types, such as digital
elevation models, digital maps, GIS layers and other pre-
processed thematic satellite images, to make the necessary
assessments.
In forestry, for example, an expert classification might identify one
stand of trees as a specific species because they grow only at
certain elevations and on southwest-facing slopes of less than 30
degrees. Another region within the image having similar spectral
values might be interpreted as grass because it only occurs next to
roadways in suburban areas. And another category may be labeled
as an orchard because the trees grow in regular patterns.
Because many of these examples rely on information contained in
data other than satellite images, it’s easy to understand that expert
system-technology is more of a decision-support tool than merely
an image classifier. In fact, a satellite image isn’t even necessary.
With the help of expert system-technology, the military already has
benefited from cross-country mobility knowledge bases that
consider soil type, land cover, elevation data and current weather
reports to determine optimal routes for a certain type of vehicle to
traverse an area. The beauty of the expert system however is that
whenever new sources of information become available, they can
be easily incorporated. For example, even though the mobility
analysis can be carried out without imagery, the accuracy of the
analysis can be affected by the ground conditions. If a satellite
image can be used to extract moisture content (i.e. the “mud”
factor), then it can be added to the knowledge base and used as
part of a rule. One other key element of the experts system is the
“traceability” of the process. Figure 6 shows that by simply
querying the resultant map, the rule that was used to create the
output can be displayed and verified.
3D information
One area of high growth is the population of 3D data into GIS
databases. This is becoming more common due to the increased
availability of stereo imagery and the drop in cost of software that
enables 3D feature extraction and model texturing.  The use of 3D
obviously helps in certain decision-making processes, but the
speed of uptake of 3D feature extraction has been phenomenal. As
computer hardware increases in capability and 3D PC games
become the norm, so the GIS industry wants to look and work in a
“real world” environment. Not only is simple 3D data required to
give slope, aspect and a range of other environmental inputs, but it
is now widely being augmented with texture based models, based
on the 3D measurements to create flythroughs and visualizations
that bring a new realism to GIS.
Quality Control of data
Capturing the spatial information though is just one part of the
process. The quality of the information needs to be checked to
ensure its accuracy. The best way of doing this is to use survey
data that is traditionally of very high accuracy. Unfortunately,
integrating survey data with GIS data in the past has been difficult.
New software is however becoming available that helps this,
allowing data from both GPS and TPS to be included in the GIS
and used to assess the accuracy of the existing data whether by
linking survey points to points in the data or by applying a “quality
measure” of the original data.
Most GIS databases were created through the digitizing of paper
maps. Whilst the results seem relatively accurate, few are checked
to ensure true absolute accuracy. In Figure 8, we can see a
“typical” GIS display, showing houses and land parcels. On first
glance it seems a good representation of what is on the ground.
However, when we overlay ground survey data as in Figure 9, it
becomes obvious that the absolute positions of the buildings on the
ground differ from their location in the database and we can easily
see the error or offset. What the new generation software will do is
enable spatial data stored in the GIS to be either corrected using
the survey data, where the actual points, lines and polygons are
shifted to their correct location, or to be “virtually” modified. This
means that the database is not actually changed, but that features
in it are “linked” to true survey points, enabling the correct
positional information to be used in any GIS process.
Maintenance
In addition to the quality control issue is the maintenance of the
data. When the database is created, only the information that is to
hand can be included. From imagery and paper maps, this may be
very simple descriptive information that is relevant to the original
map scale. What cannot be input is more subjective information
that can only be gathered by a human actually looking at the
feature and making an assessment. For instance, a forester might
want to include in his database the species of tree, its health,
ground conditions and the effect of any local pollution.
This information cannot be gathered from maps or images, it must
be collected on the ground using mobile GIS. Figure 10 shows a
typical low cost GIS oriented GPS system that enables this. These
systems, capable of 1-2 m accuracy on the ground now use
common GIS software as their data collection methods and simple
PDA hardware. The ability to customize the software allows field
based data collection to be carried out simple and easily and is
being widely used by utility companies, local governments and
environmental organizations around the world.
Future trends
The remote sensing and photogrammetric industries are going
through a massive change, becoming more closely integrated with
a fast growing and competitive GIS industry. The surveying
industry is about to enter the same phase. What is clear is that
imagery and the technology associated with preparing it for GIS
and extracting information from it is becoming a key part of GIS
systems worldwide. It is important that GIS software changes to
take account of this new and extensive user requirement and that
the industry as a whole begins to provide services that match the
demands of these new users. What we shall see over the next 2- 3
years is:
* A much broader range of imagery becoming available,
based on new and existing sources of data
* More regular revisit capabilities, enabling higher frequency
change detection and monitoring applications
* The growth of specialist services using new digital
camera/GPS technology to provide targeted, low cost aerial
surveys for specific applications
* More integrated GPS applications within GIS, imaging and
photogrammetry software to improve quality and positional
accuracy.
About the Author:
John W Allan
Leica Geosystems, GIS & Mapping Division
Telford House
Fulbourn
Cambridge CB1 6DY
United Kingdom.
Submit a technical paper or comments to
imaging@geocomm.com
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