Digital Ortho
Image Creation
                                               Figure 1 Aerial Photograph taken of
The population as reported in the 2000 U.S.
Abstract                                               Census was 193,277. The...
technology to assign points of reference across
                                                                 the image...
Steps for Autosync                                               imagery into a seamless ortho image
the Color Balancing
I found that the project really gained momentum
                                                          when we had the ...
After wading through all of these problems we
moved into the home stretch of the project
where we had to mosaic the 35 ort...

Marzan, G. T. and Karara, H. M. 1975. A computer program for direct linear transformation solution of
the coli...
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Digital Ortho Image Creation of Hall County Aerial Photos Paper


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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

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Digital Ortho Image Creation of Hall County Aerial Photos Paper

  1. 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. 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. ~ 1~
  3. 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 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) ~ 2~
  4. 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. 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. 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. ~ 5~
  7. 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. 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, (March 7, 2008) 2007, Erdas Imagine 9.1 Field Guide Volume Two, Leica Geosystems Geospatial Imaging, LLC, (March 7, 2008)