Orthorectification using

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Orthorectification using ERDAS IMAGINE
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Orthorectification Basic Concepts
Geometric distortions are present in satellite
images caused by satellite platform and its
elliptic movement around the earth, due to the
imaging sensor (parameters like focal length,
instantaneous field of view, panoramic view, and
the oblique viewing system in some cases), and
due to the earth rotation, curvature, and
topographic relief etc. Therefore, it is
essential to remove all types of geometric
distortions in RS imagery before using it for
feature/ information extraction.
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Orthorectification Definition
Orthorectification is the geometric
transformation of an image in which image
displacements due to sensor orientation and
terrain are corrected to the projection of a map
coordinate system. The accuracy of an
orthorectified image and its assigned
georeferencing information is dependent on DEM
and the quality of the sensor model.
Orthorectification is the process of reducing
geometric errors inherent within photography and
imagery. The variables contributing to geometric
errors include, but are not limited to

• camera and sensor orientation
• systematic error associated with the camera or
  sensor
• topographic relief displacement
• Earth curvature

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It is a form of rectification that corrects for
terrain displacement and can be used if there is
a DEM of the study area. It is based on
collinearity equations, which can be derived by
using 3D GCPs.
Relief displacement is corrected by taking each
pixel of a DEM and finding the equivalent
position in the satellite or aerial image. A
brightness value is determined for this location
based on resampling of the surrounding pixels.
The brightness value, elevation, and exterior
orientation information are used to calculate the
equivalent location in the ortho image file.
Note In relatively flat areas,
orthorectification is not necessary, but in
mountainous areas (or on aerial photographs of
buildings), where a high degree of accuracy is
required, orthorectification is recommended
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The input data required for orthorectification
process is the original image, an appropriate
sensor model, GCPs, and a DTM or DEM. The
orthorectification process takes the raw digital
imagery and applies a DEM and triangulation
results to create an orthorectified image. Once
an orthorectified image is created, each pixel
within the image possesses geometric reliability.
Thus, measurements taken off an orthorectified
image represent the corresponding measurements as
if they were taken on the Earths surface.
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In contrast to conventional rectification
techniques, orthorectification relies on the
digital elevation data, unless the terrain is
flat. Various sources of elevation data exist,
such as the USGS DEM and a DEM automatically
created from stereo image pairs.
For different image data, different accuracy
levels of DEMs are required to limit the
uncertainty-related errors within a controlled
limit. While the near-vertical viewing SPOT scene
can use very coarse DEMs, images with large
incidence angles need better elevation data such
as USGS level-1 DEMs. For aerial photographs with
a scale larger than 160000, elevation data
accurate to 1 meter is recommended. The 1-meter
accuracy reflects the accuracy of the Z
coordinates in the DEM, not the DEM resolution or
posting.
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Guidelines for DEM Selection for Ortho Resampling
The DEM should be large enough that the entire
area to be orthorectified is covered by the
extent of the DEM (excluding background). This
eliminates possible conflicts between zero
background value and zero data value. If the
DEM is too small to completely cover the
orthorectification area and has zero background
values and zero data values, neither of the
methods above is completely satisfactory. One way
to approach the problem would be to locate and
change zero data values to a very small number
(0.001 for Float or Double type data, or 1 for
8-bit or16-bit data) and then recompute
statistics ignoring zeros. This eliminates the
effects of the background while having minimal
effect on sea-level elevations.
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Resampling methods used are nearest neighbor,
bilinear interpolation, and cubic convolution.
Generally, when the cell sizes of ortho image
pixels are selected, they should be similar or
larger than the cell sizes of the original image.
For example, if the image was scanned 9K 9K,
one pixel would represent 0.025 mm on the image.
Assuming that the SI of this photo is 140000,
then the cell size on the ground is about 1 m.
For the orthoimage, it is appropriate to choose a
pixel spacing of 1 m or larger. Choosing a
smaller pixel size oversamples the original
image. For SPOT Pan images, a cell size of 10
meters is appropriate. Any further enlargement
from the original scene to the ortho photo does
not improve the image detail. For IRS-1C images,
a cell size of 6 meters is appropriate.
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In ERDAS IMAGINE, orthorectification can be done
by two methods

1. through Image Geometric Correction module in
   Data Preparation tool
2. through LPS tool

Note We will use the first method in our
exercise.
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Selecting the correct Resampling Method
Bilinear Interpolation Use Bilinear
interpolation when

• the DEM cell size is much greater than the image
  cell size (for example, a 30-meter DEM with
  1-meter air photo)
• the DEM covers the entire output area of the
  orthorectified image

Nearest Neighbor Use Nearest Neighbor when

• the DEM covers less than the output area of the
  orthorectified image
• the DEM cell size is approximately the same as
  the image cell size (for example, a  30-meter DEM
  with 10-meter Spot)

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Cubic Convolution Use Cubic Convolution when

• the DEM cell size is much greater than the image
  cell size (cubic convolution is very similar to
  bilinear interpolation)
• the DEM covers the entire output area of the
  orthorectified image

Bicubic Spline Interpolation Use Bicubic
Spline Interpolation when

• the DEM cell size is much greater than the image
  cell size -at least 5x5
• the DEM covers the entire output area of the
  orthorectified image

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Further Reading

• ERDAS. (2009) "ERDAS Field Guide", ERDAS, Inc.,
  USA
• Willneff, J. and Poon, J. (2006) "Georeferencing
  from Orthorectified and Non-Orthorectified
  High-Resolution Satellite Imagery". The 13th
  Australasian Remote Sensing and Photogrammetry
  Conference, 20 24 November, National Convention
  Centre, Canberra, Australia
• Parcharidis, I., Foumelis, M., Papageorgiou, E.,
  Segou, M. and Sakkas, V. (2005)
  "Ortho-rectification and Assessment of QuickBird
  Imagery using D-GPS Measurements over Paros Urban
  Area". International Archives of Photogrammetry,
  Remote Sensing and Spatial Information Sciences,
  Vol. XXXVI, PART 8/W27

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Proceed to Lab Exercise..