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
High Performance Computing for Satellite Image Processing and Analyzing – A ...Editor IJCATR
High Performance Computing (HPC) is the recently developed technology in the field of computer science, which evolved
due to meet increasing demands for processing speed and analysing/processing huge size of data sets. HPC brings together several
technologies such as computer architecture, algorithm, programs and system software under one canopy to solve/handle advanced
complex problems quickly and effectively. It is a crucial element today to gather and process large amount of satellite (remote sensing)
data which is the need of an hour. In this paper, we review recent development in HPC technology (Parallel, Distributed and Cluster
Computing) for satellite data processing and analysing. We attempt to discuss the fundamentals of High Performance Computing
(HPC) for satellite data processing and analysing, in a way which is easy to understand without much previous background. We sketch
the various HPC approach such as Parallel, Distributed & Cluster Computing and subsequent satellite data processing & analysing
methods like geo-referencing, image mosaicking, image classification, image fusion and Morphological/neural approach for hyperspectral satellite data. Collective, these works deliver a snapshot, tables and algorithms of the recent developments in those sectors and
offer a thoughtful perspective of the potential and promising challenges of satellite data processing and analysing using HPC
paradigms.
Digital Ortho Image Creation of Hall County Aerial Photosmpadams77
Powerpoint Presentation that 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
Satellite Image Processing technique to enhance raw images received from cameras or sensors placed on satellites, space probes and aircrafts or pictures taken in normal day to day life in various applications.
High Performance Computing for Satellite Image Processing and Analyzing – A ...Editor IJCATR
High Performance Computing (HPC) is the recently developed technology in the field of computer science, which evolved
due to meet increasing demands for processing speed and analysing/processing huge size of data sets. HPC brings together several
technologies such as computer architecture, algorithm, programs and system software under one canopy to solve/handle advanced
complex problems quickly and effectively. It is a crucial element today to gather and process large amount of satellite (remote sensing)
data which is the need of an hour. In this paper, we review recent development in HPC technology (Parallel, Distributed and Cluster
Computing) for satellite data processing and analysing. We attempt to discuss the fundamentals of High Performance Computing
(HPC) for satellite data processing and analysing, in a way which is easy to understand without much previous background. We sketch
the various HPC approach such as Parallel, Distributed & Cluster Computing and subsequent satellite data processing & analysing
methods like geo-referencing, image mosaicking, image classification, image fusion and Morphological/neural approach for hyperspectral satellite data. Collective, these works deliver a snapshot, tables and algorithms of the recent developments in those sectors and
offer a thoughtful perspective of the potential and promising challenges of satellite data processing and analysing using HPC
paradigms.
Digital Ortho Image Creation of Hall County Aerial Photosmpadams77
Powerpoint Presentation that 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
Satellite Image Processing technique to enhance raw images received from cameras or sensors placed on satellites, space probes and aircrafts or pictures taken in normal day to day life in various applications.
Modelled and Analysed the watershed Dynamics in Mahanadi River Basin. Finally came up with watershed Management Plan to minimise the future LUCC in Mahanadi River Basin
Graphical User Interface for Benthic MappingIDES Editor
A Graphical User Interface (GUI) was developed for
a user-friendly implementation of a water depth correction
model. The Interactive Data Language (IDL)-based tool
provides the prospective users with an interface that can be
applied to perform water depth correction on hyperspectral
images that contain shallow water bodies containing benthic
habitat information. Users can select a pixel or a subset of a
hyperspectral image to be corrected and define water
correction for water depths of 0-2.0 m and for turbidity values
of 0-20 NTU (Nephalometric Turbidity Unit) using the GUI.
The results demonstrate that the GUI is an effective benthic
mapping tool for shallow littoral areas; and it can be
incorporated as a module in currently available commercial
image processing software.
1. M.SC and PG.Diploma on Remote Sensing and Geographical information system.
2. Experience on Remote Sensing and GIS of 3 Years 11 Months.
3. 1 Year Diploma on Information Technology.
4.Certificate course On Remote Sensing & Gis From ISRO.
Improving the Estimation of Crop of Rice Using Higher Resolution Simulated La...iosrjce
IOSR Journal of Applied Physics (IOSR-JAP) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Satellite Image Based Mapping of Wetland Tundra Landscapes Using ILWIS GISUniversität Salzburg
Presentation shows an application of ILWIS GIS for RS data processing with a case study of detecting land cover changes during 20-year period (1988-2011) in Yamal Peninsula, Arctic. Research goals: Distribution of different types of landscapes in the wetland tundra of the Yamal Peninsula; Monitoring changes in the landscapes of tundra; Analysis of the landscape dynamics for 2 decades (1988-2011). Data include 2 satellite images: Landsat TM for 1988 and 2011. Methods include clustering, segmentation and classification. Technical approach: Landsat TM data processing by ILWIS GIS. Methods: Supervised classification of Landsat TM images. Results demonstrated changes in selected land cover types. Study area: tundra landscapes in the wetlands of the Yamal Peninsula in the Far North of Russia. Statistical results of calculations of types of vegetation cover were obtained in a semi-automatic mode in ILWIS GIS. In 1988 ’willow shrubs’ type covered 412,292 pixels from the total part of the AOI, and ’high willow’ class is 823,430 pixels. 2011: willow increased to 651427 pixels, (’willow shrubs’), and 893092 pixels (’high willows’). Both combined classes of willows, typical for AOI with a high water content, cover total 1544519 pixels, which is 40.27 %. Area of grasses decreased compared to shrub and willow. Max area covered by class ’heather and dry grass’ is 933798 pixels
Comparing canopy density measurement from UAV and hemispherical photography: ...IJECEIAES
UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.
Image interpretation is related with the identification of remote sensed objects or images and knowing about their significance. To see the useful result of image interpretation the primary tasks are Detection, Identification, Measurement, Problem solving.
IMAGE INTERPRETATION TECHNIQUES of surveyKaran Patel
Image interpretation is the process of examining an aerial photo or digital remote sensing image and manually identifying the features in that image. This method can be highly reliable and a wide variety of features can be identified, such as riparian vegetation type and condition, and anthropogenic features
A New Reality - Virtual Reality & Augmented Reality WorkshopTim Gentle
With Virtual Reality & Augmented Reality, now accessible, it's important to explore what this new reality can offer you. Back me up, I'm going in! Coliban Water Presentation
Modelled and Analysed the watershed Dynamics in Mahanadi River Basin. Finally came up with watershed Management Plan to minimise the future LUCC in Mahanadi River Basin
Graphical User Interface for Benthic MappingIDES Editor
A Graphical User Interface (GUI) was developed for
a user-friendly implementation of a water depth correction
model. The Interactive Data Language (IDL)-based tool
provides the prospective users with an interface that can be
applied to perform water depth correction on hyperspectral
images that contain shallow water bodies containing benthic
habitat information. Users can select a pixel or a subset of a
hyperspectral image to be corrected and define water
correction for water depths of 0-2.0 m and for turbidity values
of 0-20 NTU (Nephalometric Turbidity Unit) using the GUI.
The results demonstrate that the GUI is an effective benthic
mapping tool for shallow littoral areas; and it can be
incorporated as a module in currently available commercial
image processing software.
1. M.SC and PG.Diploma on Remote Sensing and Geographical information system.
2. Experience on Remote Sensing and GIS of 3 Years 11 Months.
3. 1 Year Diploma on Information Technology.
4.Certificate course On Remote Sensing & Gis From ISRO.
Improving the Estimation of Crop of Rice Using Higher Resolution Simulated La...iosrjce
IOSR Journal of Applied Physics (IOSR-JAP) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Satellite Image Based Mapping of Wetland Tundra Landscapes Using ILWIS GISUniversität Salzburg
Presentation shows an application of ILWIS GIS for RS data processing with a case study of detecting land cover changes during 20-year period (1988-2011) in Yamal Peninsula, Arctic. Research goals: Distribution of different types of landscapes in the wetland tundra of the Yamal Peninsula; Monitoring changes in the landscapes of tundra; Analysis of the landscape dynamics for 2 decades (1988-2011). Data include 2 satellite images: Landsat TM for 1988 and 2011. Methods include clustering, segmentation and classification. Technical approach: Landsat TM data processing by ILWIS GIS. Methods: Supervised classification of Landsat TM images. Results demonstrated changes in selected land cover types. Study area: tundra landscapes in the wetlands of the Yamal Peninsula in the Far North of Russia. Statistical results of calculations of types of vegetation cover were obtained in a semi-automatic mode in ILWIS GIS. In 1988 ’willow shrubs’ type covered 412,292 pixels from the total part of the AOI, and ’high willow’ class is 823,430 pixels. 2011: willow increased to 651427 pixels, (’willow shrubs’), and 893092 pixels (’high willows’). Both combined classes of willows, typical for AOI with a high water content, cover total 1544519 pixels, which is 40.27 %. Area of grasses decreased compared to shrub and willow. Max area covered by class ’heather and dry grass’ is 933798 pixels
Comparing canopy density measurement from UAV and hemispherical photography: ...IJECEIAES
UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.
Image interpretation is related with the identification of remote sensed objects or images and knowing about their significance. To see the useful result of image interpretation the primary tasks are Detection, Identification, Measurement, Problem solving.
IMAGE INTERPRETATION TECHNIQUES of surveyKaran Patel
Image interpretation is the process of examining an aerial photo or digital remote sensing image and manually identifying the features in that image. This method can be highly reliable and a wide variety of features can be identified, such as riparian vegetation type and condition, and anthropogenic features
A New Reality - Virtual Reality & Augmented Reality WorkshopTim Gentle
With Virtual Reality & Augmented Reality, now accessible, it's important to explore what this new reality can offer you. Back me up, I'm going in! Coliban Water Presentation
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Geospatial Data Acquisition Using Unmanned Aerial SystemsIEREK Press
The Rivers State University campus in Portharcourt is one of the university campuses in the city of Portharcourt,
Nigeria covering over 21 square kilometers and housing a variety of academic, residential, administrative and other
support buildings. The University Campus has seen significant transformation in recent years, including the
rehabilitation of old facilities, the construction of new academic facilities and the most recent update on the creation
of new collages, faculties and departments. The current view of the transformations done within the University
Campus is missing from several available maps of the university. Numerous facilities have been constructed on the
University Campus that are not represented on these maps as well as the qualities associated with these facilities.
Existing information on the various landscapes on the map is outdated and it needs to be streamlined in light of
recent changes to the University's facilities and departments. This research article aims to demonstrate the
effectiveness of unmanned aerial systems (UAS) in geospatial data collection for physical planning and mapping of
infrastructures at the Rivers State University Port Harcourt campus by developing a UAS-based digital map and
tour guide for RSU's main campus covering all collages, faculties and departments and this offers visitors, staff and
students with location and attribute information within the campus.
Methodologically, Unmanned Aerial Vehicles were deployed to obtain current visible images of the campus
following the growth and increasing infrastructural development. At a flying height of 76.2m (250 ft), a DJI
Phantom 4 Pro UAS equipped with a 20-megapixel visible camera was flown around the campus, generating
imagery with 1.69cm spatial resolution per pixel. To obtain 3D modeling capabilities, visible imagery was acquired
using the flight-planning software DroneDeploy with a near nadir angle and 75 percent front and side overlap.
Vertical positions were linked to the World Geodetic System 1984 and horizontal positions to the 1984 World
Geodetic Datum universal transverse Mercator (UTM) (WGS 84). To match the UAS data, GCPs were transformed
to UTM zone 32 north.
Finally, dense point clouds, DSM, and an orthomosaic which is a geometrically corrected aerial image that provides
an accurate representation of an area and can be used to determine true distances, were among the UAS-derived
deliverables.
Remote sensing and geographic information systems (GIS) analysis involves the use of technology to gather, manipulate, and analyze spatial data to understand a range of phenomena. Remote sensing entails obtaining information about the Earth's surface by examining data acquired by a device, which is at a distance from the surface, most often satellites orbiting the earth and airplanes. GIS are computer-based systems that are used to capture, store, analyze, and display geographic information. These two approaches are used widely, often together, to assess natural resources and monitor environmental changes. Social scientists can gain insights into fine spatial and temporal dynamics of a range of social phenomena in environmental contexts by analyzing time series of remote sensing data, by linking remote sensing to socioeconomic data using GIS, and developing with these data a range of digital models and analyses. This article examines remote sensing and GIS in general, with an emphasis on the former, and then explores how these approaches may be used together to address a range of issues. It also emphasizes the role of remote sensing and GIS for use by scientists, engineers & geologists in water resources management
A geographic information system (GIS) is a computer-based tool for mapping and analyzing features and events on earth. On the other hand, remote sensing is the science of collecting data regarding an object or a phenomenon without any physical contact with the object
Satellite image processing is a technique to enhance raw images received from cameras or sensors placed on satellites, space probes and aircrafts or pictures taken in normal day to day life in various applications. The process of creating thematic maps as spatial distribution of particular information. These are structured by Spectral Bands. These have constant density and when they overlap their densities get added. It performs image analysis on multiple scale images and catches the comprehensive information of system for different application. Examples of themes are soil, vegetation, water-depth and air. The supervising of such critical events requires a huge volume of surveillance data and extremely powerful real time processing for infrastructure
Digital Image Processing is the manipulation of the digital data with the help of the computer hardware and software to produce digital maps in which specific information has been extracted and highlighted. Visual image interpretation techniques have certain disadvantages and may require extensive training and are labor intensive.
In this technique, the spectral characteristics are not always fully evaluated because of the limited ability of the eye to discern tonal value and analyze the spectral changes.
If the data are in digital mode, the remote sensing data can be analyzed using digital image processing techniques and such a data base can be used in Raster GIS.
In applications where spectral patterns are more informative, it is preferable to analyze digital data rather than pictorial data.
Satellite remote sensing data in general and digital data in particular have been used as basic inputs for the inventory and mapping of natural resources of the earth surface like forestry, soils, geology and agriculture.
Space borne remote sensing data suffer from a variety of radiometric, atmospheric and geometric errors, earth‟s rotation and so on.
These distortions would diminish the accuracy of the information extracted and reduce the utility of the data. So these errors required to be corrected.
Digital Ortho Image Creation of Hall County Aerial Photos Paper
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.
~ 1~
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)
~ 2~
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.
~ 5~
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)