Digital Ortho Image Creation of Hall County Aerial Photos

Figure 1 Aerial Photograph taken of Gainesville College in 1980.

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

Abstract

The Hall County National Resource Conservation Service (NRCS) has several 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 outlines the methods used in digitizing, georeferencing, orth0-rectifying, and mosaicking a set of thirty-five images taken October 12, 1980. This project was made possible by support from the Institute for Environmental Spatial Analysis at Gainesville State College and from a grant provided by the Georgia View Consortium.

Introduction “Nothing has such power to broaden the mind as the ability to investigate systematically and truly all that comes under thy observation in life.” (Marcus Aurelius) 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 the importance of preserving the vast number of aerial photographs that have been taken over the last century. These air photos are a clear recollection of the land as it was at that moment in time. We can use this data to increase our understanding of key features of the land including; forests, watersheds, agriculture and urban areas. Land Use studies of this kind 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 studies of this kind of data have resulted in a better understanding of the areas where these kinds of photos have been collected.

Study Area Hall County encompasses approximately 394 square miles of land and is split by the Chattahoochee River and Lake Sydney Lanier.

The population as reported in the 2000 U.S. Census was 193,277. The county has seen tremendous growth during the last twenty years and this imagery shows the county as it was in 1980.

Data Acquisition The 1980 aerial image set includes (35) 24”x 24” hard copy grayscale images flown for the U.S. Department of Agriculture by Harris Aerial Surveys Inc., Mountain Home, Arkansas, using a 6” camera. The images have a 1:40,000 spatial resolution and have been maintained by the Hall 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 color digital ortho-photos with a 2 meter spectral resolution. The Digital Elevation Model (DEM) was provided by the Georgia GIS Clearinghouse which has a wealth of GIS data for public use. The DEM has a resolution of 30 meters. We searched for a lower resolution DEM and this was the best that we could find cost free.

Methods The process of converting an aerial photograph into a digital ortho-rectified mosaic requires 3 major steps which are outlined in Figure 2.

Figure 2

Raw Image Digitizing

Georeferencing and Ortho-Rectificaton

Mosaicking

Ortho Image

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Figure 3 shows the area surrounding Gainesville State College.

Digitizing Digitizing is the process of converting an analog image or map into a digital format. (Leica Geosystems, LLC, 2007) Each photograph was 24” x 24” inches in size and could not be scanned on a standard flat bed scanner. We utilized “Gainesville Whiteprint” (a local printing company) in the digitization of these images because it was the most cost effective way to manage these images. They specialize in commercial printing and also have a large format scanner which is commonly used for engineering blueprints. We had the photographs scanned at 300 dots per inch (dpi). According to (Aronoff 2005), an aerial photograph taken with a spatial resolution of 1:40,000 would have a ground pixel resolution of 3.39 meters when scanned at 300(dpi). Figure 3 shows image 304 after it was digitized.

Georeferencing and Orthorectification

In remote sensing geoeferencing is the process of taking an image and assigning it geographic coordinates in the X and Y planes. We used the Autosync extension for Erdas Imagine 9.1. Autosync uses Automatic Point Matching (APM)

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 based on these initial points chosen by the user.

Figure 4 A representation of the X, Y, and Z axes. Image from http://www.staff.amu.edu.pl/~romangoc/graphics/M1/displacement-velocity-advanced/M1.2.gif

Orthorectification is a process to correct an aerial photo for topographic relief, lens distortion, and camera tilt; it also makes the image true to scale as if it were a map. (Leica Geosystems, LLC, 2007) In this process we used a 30 meter Hall County Digital Elevation Model (DEM), to correct the images for changes in elevation. In a DEM each pixel is assigned an elevation value or a fixed value in the Z plane. The orthorectification process occurs simultaneously, with the georeferencing process in Autosync. using the Direct Linear Transform 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 orthoreferencing is different from georeferencing. If we did not orthorectify this imagery it would have too many distortions making them unusable for land cover change analysis. The Hall County DEM is shown in Figure 6.

Gainesville State College

The Title and Black borders were cropped using Adobe Photoshop CS3 before georeferencing.

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Steps for Autosync

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.

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 5

3. The orthometric correction occurs when we utilize the Hall County DEM shown in Figure 6.

4. After the images are both geo- and ortho-corrected we then mosaicked the

imagery into a seamless ortho image that includes the complete set of images.

Figure 6

Mosaicing

Mosaicing is the process of joining several

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 used a smoothing and feathering filter at 0.5 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 running the mosaic we used the Exclude Areas tool which allowed us to create temporary Areas of Interest (AOI) files to be excluded from the statistics and histogram calculations used to perform the other image enhancement tools. We then Color Balanced the images in a linear fashion. This function attempts to remove brightness variations found across the mosaic.

Folder Name File Type

Project Files .lap Output Orthos .img Summary Reports .html

Table 1

Mr. Sid Reference

Image Raw

Image

Corresponding GCP’s

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Figure 7 Image shows “Hot Spot” reflection from Lake Lanier.

Finally, we used the Histogram Matching function which creates a new histogram for all of the images to be mosaicked by matching them to one another. A description of each tool is found in Table 2. (Erdas Imagine Online Field Guide Vol. 1 and Vol. 2)

Tool Function Smoothing Filter The process of

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 processed, they will not influence the Histogram Matching, Image Dodging, and

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 Matching

The process of 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 9.1 Online Field Guide Vol.1 and Vol. 2). Figure 8 shows the Mosaic Tool interface where all of the above user options can be accessed. The mosaic process required several hours to complete because of the size of all of the raster data files to be joined. We attempted several 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 method gave the best overall appearance to the mosaicked ortho-image and required the least amount of time to complete.

Figure 8

Figure 9 shows the mosaiced ortho-image which is now suitable for scientific analyses.

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Figure 9 Finished Digital Ortho Mosaic.

Discussion of Problems Encountered In every phase of this project we encountered challenges that had to be overcome. Some challenges were minor setbacks to the project and others became much more time consuming problems to conquer. In the beginning of this project we planned to scan each 24” x 24” image using a 12” x 18” flatbed scanner. To do this we planned to scan each raw image 8 times and then use Adobe Photoshop CS3 to stitch the scans together to create 1 digitized image. After 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 any given time. Table 3 shows the Track Log that I created to track the progress of the project.

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 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 imagery only covered Hall County. Our first approach to this problem was to clip the reference area to the boundaries of each individual county Mr. Sid file and then rectify each image once for each county that the image resided. The simplest solution was to use the basic Image Geometric Correction process using the DLT Method from the Imagine viewer. All three Mr. Sid County files could be opened in one viewer to reference the overlapping areas of the last two raw images. This process was more time consuming because we could not use Autosync and its APM function to generate GCP’s.

Figure 10 Shows Area of Overlap between Raw Images and Reference Imagery.

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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 the imagery. One method was to break the 35 images into groups of 5 and mosaic each group. After all groups were completed the groups were then mosaicked together to get the finished image. This method produced areas that were darker than we desired. I believe this was caused by color balancing across each group of images instead of across every single image. The second method we tried was to split the 35 images into 2 groups of 17. After processing the 2 groups we discovered that because of the irregular shape of the two groups we were left with areas that were clipped from the data that appeared black. Finally, we decided to run all of the images at one time and color correct each image individually within the Mosaic Tool Interface. This method produced the best results.

1980 Hall County Photo Rectification Project

# Image Number APM APM2 RMSE 1 193 √ma √ma 0.497633 2 195 √ma √ma 0.452807 3 197 √ma √ma 0.494247 4 199 √ma √ma 0.479750 5 242 √ma √ma 0.496937 6 244 √ma √ma 0.473574 7 246 √ma √ma 0.492451 8 248 √ma √ma 0.498965 9 250 √ma √ma 0.476887 10 256 √ma √ma 0.482170 11 258 √ma √ma 0.491718 12 260 √ma √ma 0.496631 13 262 √ma √ma 0.463692 14 264 √ma √ma 0.497523 15 266 √ma

Conclusion √ma 0.487930

16 268 √ma "Character cannot be developed in ease and quiet. Only through experience of trial and suffering can the soul be strengthened, ambition inspired, and success achieved." (Helen Keller)

√ma 0.393797 17 296 √ma √ma 0.475207 18 298 √ma √ma 0.493335 19 300 √ma √ma 0.394306 20 302 √ma

We believe this quotation describes our experiences throughout this project. We have gained a greater understanding of the requirements necessary to bring a project of this scope to fruition. We cannot quantify the growth this project required of us as students of GIS. This project increased our understanding of topics covered in Remote Sensing and Digital Image Processing at Gainesville State College. We hope that our experiences here will help others who are interested in similar projects in the future. Our project has preserved a piece of Hall County history forever. In doing so we have grown to understand the organization of thought and the processes required to keep a project of this scope moving forward. We will apply these experiences to future endeavors and will look back on this experience for years to come.

√ma 0.478836 21 304 pt pt 0.478324 22 306 √ma √ma 0.450521 23 308 √ma √ma 0.495473 24 313 √ma √ma 0.477157 25 315 √ma √ma 0.430584 26 317 √ma √ma 0.465306 27 319 pt pt 0.498439 28 321 pt pt 0.343481 29 323 √ma √ma 0.492303 30 329 √ma √ma 0.474994 31 331 √ma √ma 0.496858 32 333 √ma √ma 0.487706 33 335 √ma √ma 0.489472 34 337 pt

pt 0.193500

35 344 pt

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0.069100 pt Table 3

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)