Special Topics Project Paper “Digital Ortho Image Creation of Hall County Aerial Photos” which I presented at the Florida Academy of Science and Georgia Academy of Science Joint Conference held in Jacksonville, FL March 14th and 15th of 2008

1. Digital Ortho
Image Creation
Figure 1 Aerial Photograph taken of
Gainesville College in 1980.
of Hall County
Aerial Photos
Photos taken October 12, 1980
Michael Adams, Patrick Taylor and J.B.
Sharma. The Institute for Environmental
Spatial Analysis, Gainesville State College,
Gainesville, GA 30503
3/5/2008

2. The population as reported in the 2000 U.S.
Abstract Census was 193,277. The county has seen
tremendous growth during the last twenty years
The Hall County National Resource and this imagery shows the county as it was in
Conservation Service (NRCS) has several 1980.
sets of historic aerial imagery. The
purpose of this project was to digitize
these images such that the public can
utilize them for perpetuity. The project Data Acquisition
outlines the methods used in digitizing,
georeferencing, orth0-rectifying, and
mosaicking a set of thirty-five images The 1980 aerial image set includes (35) 24”x 24”
taken October 12, 1980. This project was hard copy grayscale images flown for the U.S.
made possible by support from the Department of Agriculture by Harris Aerial
Institute for Environmental Spatial Surveys Inc., Mountain Home, Arkansas, using a
Analysis at Gainesville State College and 6” camera. The images have a 1:40,000 spatial
from a grant provided by the Georgia resolution and have been maintained by the Hall
View Consortium. County NRCS since the time they were flown.
The Mr. Sid files used to georeference the aerial
photos were provided by the National
Agriculture Imagery Program. They are true
Introduction color digital ortho-photos with a 2 meter
spectral resolution. The Digital Elevation Model
(DEM) was provided by the Georgia GIS
“Nothing has such power to broaden the Clearinghouse which has a wealth of GIS data
mind as the ability to investigate for public use. The DEM has a resolution of 30
systematically and truly all that comes meters. We searched for a lower resolution
under thy observation in life.” (Marcus DEM and this was the best that we could find
Aurelius) cost free.
In this project we seek to preserve a set of 1980
vintage air photos housed by the Hall County
NRCS. We hope to increase public awareness of Methods
the importance of preserving the vast number of
aerial photographs that have been taken over the
last century. These air photos are a clear The process of converting an aerial photograph
recollection of the land as it was at that moment into a digital ortho-rectified mosaic requires 3
in time. We can use this data to increase our major steps which are outlined in Figure 2.
understanding of key features of the land
including; forests, watersheds, agriculture and
urban areas. Land Use studies of this kind Raw Image Digitizing
require digital georeferenced ortho imagery.
The ability to use this imagery to study temporal
changes of land has grown tremendously as the
technology and software has advanced. Several Georeferencing
studies of this kind of data have resulted in a Mosaicking and Ortho-
Rectificaton
better understanding of the areas where these
kinds of photos have been collected.
Ortho
Study Area Image
Figure 2
Hall County encompasses approximately 394
square miles of land and is split by the
Chattahoochee River and Lake Sydney Lanier.
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3. technology to assign points of reference across
the image after some initial user input. We used
a Ground Control Point (GCP) approach to
geometrically correcting the imagery. This
process requires the user to select points in each
image that appear to be in the same place in
space. Autosync then generates more GCP’s
Gainesville State College based on these initial points chosen by the user.
The Title and Black borders were cropped using
Adobe Photoshop CS3 before georeferencing.
Figure 3 shows the area surrounding
Gainesville State College.
Digitizing
Figure 4 A representation of the X, Y, and Z
Digitizing is the process of converting an analog axes. Image
image or map into a digital format. (Leica from http://www.staff.amu.edu.pl/~romango
Geosystems, LLC, 2007) c/graphics/M1/displacement-velocity-
Each photograph was 24” x 24” inches in size advanced/M1.2.gif
and could not be scanned on a standard flat bed
scanner. We utilized “Gainesville Whiteprint” Orthorectification is a process to correct an
(a local printing company) in the digitization of aerial photo for topographic relief, lens
these images because it was the most cost distortion, and camera tilt; it also makes the
effective way to manage these images. They image true to scale as if it were a map. (Leica
specialize in commercial printing and also have Geosystems, LLC, 2007) In this process we used
a large format scanner which is commonly used a 30 meter Hall County Digital Elevation Model
for engineering blueprints. We had the (DEM), to correct the images for changes in
photographs scanned at 300 dots per inch (dpi). elevation. In a DEM each pixel is assigned an
According to (Aronoff 2005), an aerial elevation value or a fixed value in the Z plane.
photograph taken with a spatial resolution of The orthorectification process occurs
1:40,000 would have a ground pixel resolution simultaneously, with the georeferencing process
of 3.39 meters when scanned at 300(dpi). in Autosync. using the Direct Linear Transform
Figure 3 shows image 304 after it was digitized. method. The DLT method combines the X, Y,
and Z coordinates simultaneously when
collecting GCP’s. (Marzan, G. T. and Karara, H.
M., 1975) It is important to understand how
Georeferencing and orthoreferencing is different from
Orthorectification georeferencing. If we did not orthorectify this
imagery it would have too many distortions
making them unusable for land cover change
In remote sensing geoeferencing is the process analysis. The Hall County DEM is shown in
of taking an image and assigning it geographic Figure 6.
coordinates in the X and Y planes. We used the
Autosync extension for Erdas Imagine 9.1.
Autosync uses Automatic Point Matching (APM)
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4. Steps for Autosync imagery into a seamless ortho image
that includes the complete set of images.
1. Create a file storage system for saving
the Autosync project (.lap), output
(.img), and summary report files (.html).
We created file folders as listed in Table
1.
Folder Name File Type
Project Files .lap
Output Orthos .img
Summary Reports .html
Table 1
2. Using the (naip_1-
1_2n_ga139_2006_1.sid) reference
image we georeferenced the raw aerial
imagery. Figure 5 shows the Autosync
Interface. Figure 6
Mosaicing
Mr. Sid
Raw Reference Mosaicing is the process of joining several
Image Image smaller overlapping images into one larger
seamless image. In this process several
functions are used to generate a seamless image
appearing to have been taken at one time and
not as several individual images. The amount of
data that has to be processed is especially large
when using raster datasets as the ones used in
this project. Each of our digital images were
approximately 300 Mb. We utilized the Mosaic
Tool in Erdas Imagine 9.1. In this process all 35
images were brought into the tool. From here
we created new cutlines. Cutlines are the seams
at which the images are joined together. We also
Corresponding used a smoothing and feathering filter at 0.5
GCP’s pixels width. The Smoothing Filter applies a
blurring filter along each side of the newly
generated cutlines. The Feathering Filter
softens the edges of the cutline by blending all of
the pixels within a fixed distance. Before
Figure 5 running the mosaic we used the Exclude Areas
tool which allowed us to create temporary Areas
3. The orthometric correction occurs when of Interest (AOI) files to be excluded from the
we utilize the Hall County DEM shown statistics and histogram calculations used to
in Figure 6. perform the other image enhancement tools.
We then Color Balanced the images in a linear
fashion. This function attempts to remove
4. After the images are both geo- and
brightness variations found across the mosaic.
ortho-corrected we then mosaicked the
~ 3~

5. the Color Balancing
calculations.
Color Balancing This function attempts
to remove the
brightness variations
in images before they
are mosaicked by
assuming the
variations can be
modeled as a surface.
Histogram The process of
Matching determining a lookup
table that converts the
histogram of one band
of an image to
resemble another
histogram.
Table 2 Definitions from the (Erdas Imagine
Figure 7 Image shows “Hot Spot” reflection 9.1 Online Field Guide Vol.1 and Vol. 2).
from Lake Lanier.
Finally, we used the Histogram Matching Figure 8 shows the Mosaic Tool interface where
function which creates a new histogram for all of all of the above user options can be accessed.
the images to be mosaicked by matching them to The mosaic process required several hours to
one another. A description of each tool is found complete because of the size of all of the raster
in Table 2. (Erdas Imagine Online Field Guide data files to be joined. We attempted several
Vol. 1 and Vol. 2) methods to decrease production time of this
step. In the end the fastest method was to
process all of the images at one time. This
Tool Function method gave the best overall appearance to the
mosaicked ortho-image and required the least
Smoothing Filter The process of amount of time to complete.
applying a blurring
filter along both sides
of the cutline to soften
the transition between
the mosaicked images.
Feathering Filter The process of
softening the edges
along the cutline of the
mosaicked images by
blending all of the
pixels within a set
distance.
Exclude Areas Allows the user to
create Areas of
Interest which are
excluded from the
statistics and
histogram
calculations on which
the processes depend.
Although they will be Figure 8
processed, they will
not influence the
Histogram Matching, Figure 9 shows the mosaiced ortho-image which
Image Dodging, and is now suitable for scientific analyses.
~ 4~

6. I found that the project really gained momentum
when we had the ability to know exactly where
the project was at any given time. The scope of
this project required careful attention to detail at
each stage. Once the project progressed to this
point it was time to begin the Georeferencing
and Orthorectification process. We had little
experience with Autosync at this point in the
project. We had to research Autosync and teach
ourselves the best settings for creating GCP’s.
The APM function would take approximately an
hour to process and then we manually discarded
any erroneous points that were left. This process
requires the user to sift through a large amount
of computer generated GCP’s. From these we
would wean it down to 25 to 35 points before
processing/calibrating the image. We set our
goal of 0.5 pixels of Root Mean Square Error
(RMSE). I believe that with this kind of imagery
Figure 9 Finished Digital Ortho Mosaic. 1 pixel of error would have been close enough.
We also ran into a problem with the last two
images. The last 2 images were in the
intersection of three counties. Our reference
Discussion of Problems imagery only covered Hall County. Our first
Encountered approach to this problem was to clip the
reference area to the boundaries of each
individual county Mr. Sid file and then rectify
In every phase of this project we encountered each image once for each county that the image
challenges that had to be overcome. Some resided. The simplest solution was to use the
challenges were minor setbacks to the project basic Image Geometric Correction process using
and others became much more time consuming the DLT Method from the Imagine viewer. All
problems to conquer. In the beginning of this three Mr. Sid County files could be opened in
project we planned to scan each 24” x 24” image one viewer to reference the overlapping areas of
using a 12” x 18” flatbed scanner. To do this we the last two raw images. This process was more
planned to scan each raw image 8 times and time consuming because we could not use
then use Adobe Photoshop CS3 to stitch the Autosync and its APM function to generate
scans together to create 1 digitized image. After GCP’s.
much effort and discouragement we decided this
method would not provide the results wanted.
We saved time and money by outsourcing our
digitization needs to a local printing company.
The next problem we faced dealt with splitting
the project into manageable pieces and creating
a system that would allow multiple people to
work on the project independently without the
need for direct communication. Initially we
found that we were working on the same images
at the same time in different user folders. At
that time I decided to create an iron clad naming
system and to save each of the different
Autosync generated file types in different folders
so that they could be accessed with greater ease.
I also created a track log so that we could see
what the other team member was working on at Figure 10 Shows Area of Overlap between Raw
any given time. Table 3 shows the Track Log Images and Reference Imagery.
that I created to track the progress of the project.
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7. After wading through all of these problems we
moved into the home stretch of the project
where we had to mosaic the 35 orthorectified
images. We tried several methods of mosaicking 1980 Hall County Photo Rectification
the imagery. One method was to break the 35
images into groups of 5 and mosaic each group.
Project
After all groups were completed the groups were # Image Number APM APM2 RMSE
then mosaicked together to get the finished
image. This method produced areas that were 1 193 √ma √ma 0.497633
darker than we desired. I believe this was 2 195 √ma √ma 0.452807
caused by color balancing across each group of 3 197 √ma √ma 0.494247
images instead of across every single image. The
4 199 √ma √ma 0.479750
second method we tried was to split the 35
images into 2 groups of 17. After processing the 5 242 √ma √ma 0.496937
2 groups we discovered that because of the 6 244 √ma √ma 0.473574
irregular shape of the two groups we were left 7 √ma √ma 0.492451
246
with areas that were clipped from the data that
appeared black. Finally, we decided to run all of 8 248 √ma √ma 0.498965
the images at one time and color correct each 9 250 √ma √ma 0.476887
image individually within the Mosaic Tool 10 256 √ma √ma 0.482170
Interface. This method produced the best
results.
11 258 √ma √ma 0.491718
12 260 √ma √ma 0.496631
13 262 √ma √ma 0.463692
Conclusion 14 264 √ma √ma 0.497523
15 266 √ma √ma 0.487930
"Character cannot be developed in ease and
quiet. Only through experience of trial and
16 268 √ma √ma 0.393797
suffering can the soul be strengthened, ambition 17 296 √ma √ma 0.475207
inspired, and success achieved." (Helen Keller) 18 298 √ma √ma 0.493335
19 300 √ma √ma 0.394306
We believe this quotation describes our
experiences throughout this project. We have 20 302 √ma √ma 0.478836
gained a greater understanding of the 21 304 pt pt 0.478324
requirements necessary to bring a project of this 22 √ma √ma 0.450521
306
scope to fruition. We cannot quantify the
growth this project required of us as students of 23 308 √ma √ma 0.495473
GIS. This project increased our understanding 24 313 √ma √ma 0.477157
of topics covered in Remote Sensing and Digital 25 315 √ma √ma 0.430584
Image Processing at Gainesville State College.
26 317 √ma √ma 0.465306
We hope that our experiences here will help
others who are interested in similar projects in 27 319 pt pt 0.498439
the future. Our project has preserved a piece of 28 321 pt pt 0.343481
Hall County history forever. In doing so we have 29 √ma √ma 0.492303
323
grown to understand the organization of thought
and the processes required to keep a project of 30 329 √ma √ma 0.474994
this scope moving forward. We will apply these 31 331 √ma √ma 0.496858
experiences to future endeavors and will look 32 333 √ma √ma 0.487706
back on this experience for years to come.
33 335 √ma √ma 0.489472
34 337 pt pt 0.193500
35 344 pt pt 0.069100
Table 3
~ 6~

8. References
Marzan, G. T. and Karara, H. M. 1975. A computer program for direct linear transformation solution of
the colinearity condition, and some applications of it. Proceedings of the Symposium on Close-Range
Photogrammetric Systems, pp. 420-476. American Society of Photogrammetry, Falls Church.
Aronoff, Stan, 2005, Remote Sensing for GIS Managers, ESRI Press, Redlands, California, 487 p.
2007, Erdas Imagine 9.1 Field Guide Volume One, Leica Geosystems Geospatial Imaging,
LLC, http://gi.leica-geosystems.com/documents/pdf/FieldGuide_Vol1.pdf (March 7, 2008)
2007, Erdas Imagine 9.1 Field Guide Volume Two, Leica Geosystems Geospatial Imaging,
LLC, http://gi.leica-geosystems.com/documents/pdf/FieldGuide_Vol2.pdf (March 7, 2008)