Project Overview

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Project Overview
COMPARISON BETWEEN AERIAL DIGITAL ORTHOPHOTO AND
SATELLITE IMAGES

• GISDATA D.O.O.
• Ivana Lampek Pavcnik, ivana.lampek_at_gisdata.hr

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Project Goals

• COMPARISON
• Quality of geometrical corrections
• Quality of Interpretability
• Time for defining and ordering
• Time for geometric correction
• Price

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Description

• The focus of the project was to examine the
  results of different type comparisons and discuss
  the advantages or disavantages between aerial and
  satellite images

FOR MORE INFO...
See the final report and procesed data
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Data used in project

• Aerial black/white photos
• Area of interest city Karlovac and environment
• Area 42000 m2
• Scale of expose 120000
• Number of frames 10, spatial resolution0,5m
• Date
• For the frame 317 04. 05. 2000.
• For the frame 2/1 29. 02. 2000

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Data used in project

• Color aerial photos
• Area of interest city Karlovac and environment
• Area 27000 m2
• Scale of expose 120000
• Number of frames 6, spatial resolution0,5m
• Date
• May, 2002.

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Data used in project

• IKONOS satellite images
• Area of interest city Karlovac and environment
• Area 57 000 m2
• Number of frames 2, spatial resolution1m
• Date
• May, 2003.

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Technology

• Digital Photogrammetry
• for geometrical corrections

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Digital Photogrammetry

• Goal Creating Orthos
• Means
• Aerial Triangulation
• Orthorectification

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Why ORTHOrectify?

• There are geometric errors associated with
  satellite images and aerial photographs
• Errors are caused by
• Scale Variation
• Sensor Attitude/Orientation
• Internal Sensor Errors
• Orthorectification removes these errors

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Scale Variation
House width 8m
Scale is 1400
Scale is 1133
Scale varies across the photography
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Scale Variation
House width constant (8m), width in photographs
varies, therefore scale varies
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Differences between aerial triangulations

• Aerial photosgt to establish Image Coordinates

• Provided in a Camera Calibration Certificate
• Parameters defining this geometry are
• Focal length
• Radial Lens Distortion
• Principal Point
• Fiducial Coordinates

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Internal Geometry of satellite

• Usually the internal parameters are read from the
  image header (SPOT, IRS)
• Focal length
• Principal point Xo, Yo
• Pixel Size
• Number of sensor Columns
• In the IKONOS and Quick Bird case, the geometry
  is modeled using rational polynomials
• User does not need to define these

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Flight Line Characteristics for aerial photos
A block should have at least one pair of images
that overlap
IKONOS or Quick Bird images do not need to have
at least one pair of images that overlap
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Acquiring Ground GCP Coordinates

• Coordinates of GCPs in external orientation can
  be gathered using various techniques
• Using GPS
• From Maps
• From other rectified imagery
• Should have X, Y and Z values for overlapping
  aerial images
• Should have X, Y for satellite images and Z
  values from DEM

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The Influence of Quality Estimates
Adjustment process will move points until the
best solution is found
Inputted Standard Deviations (Measures of Quality)
The points fluctuate with weighted limits as
specified by the standard deviation values
Adjustment takes places in the X, Y AND Z
direction
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Block Residuals

• Block of eight images

• Image ground measurements

• Least Squares Adjustment calculates new points
  based on distributing and minimizing residuals
  throughout the ENTIRE block

• There are RESIDUALS for
• - Each ground point
• - Each image point
• - Each perspective center

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Block Residuals for all data sources

• Color Aerial images
• mX mY mZ 0.3239 0.309
  0.6507

• BW Aerial images
• mX mY mZ
• 0.4175 0.4641 0.4220

• IKONOS
• mX mY mZ
• 0.3879 0.3687 DEM -accuracy

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RESULTS of geometrical correction
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1. Pixel in the DEM (Height)
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2. Parameters of Interior and Exterior Orientation
3. In the image, a brightness value is determined
based on the resampling of surrounding pixels
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The orthographic image is constructed by
resampling the original image pixels into their
new orthorectified positions
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4. Height, Interior and Exterior Orientation
information and Brightness Value are used to
calculate equivalent location in the Ortho Image
Orthographic Projection
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Digital terrain model

• - 32 digitized maps with scale 1 5000
• - equidistance 5m
• summary 125 878 arcova for generating the
  surface model

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Digital orthophotos

• CORRECTED Images as result of ortorectification
  process

• The software takes each DEM pixel and finds the
  equivalent position in the image. A brightness
  value is calculated based on the surrounding
  pixels. This brightness value, the elevation, the
  interior orientation and exterior orientation
  information is used to calculate the equivalent
  location on the ortho image

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Quality of interpretability

• Automatic interpretation
• Defining the level of
• Image Interpretability Rating Scales

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Automatic interpretation

• Seed propertiesgt

Neighborhood This option determines which pixels
will be considered contiguous to the seed pixel.
Any neighbor pixel that meets all selection
criteria is accepted and thus, itself, becomes a
seed pixel.        If four neighbors are
searched, then only those pixels above, below, to
the left, and to the right of the seed pixel are
considered contiguous.        If eight
neighbors are searched then the diagonal pixels
are also considered contiguous. Geographic
Constraints This group allows you to enter
constraints for the AOI. You can select only one
option or use both options. Area The maximum
size of the AOI Distance specifying a distance
from the seed pixel. Spectral Euclidean
Distance The Euclidean spectral distance in
digital number (DN) units on which to accept
pixels. The pixels that are accepted will be
within this spectral distance from the mean of
the seed pixel.
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SED 10, AREA5000 pixels
SED 49, AREA5000 pixels
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SED 50, AREA5000pixels
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SED 10, AREA5000 pixels
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SED 13, AREA5000 pixels
SED 10, AREA5000 p
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UNSUPERVISED CLASSIFICATION

• ISODATA algorithm to perform an unsupervised
  classification. ISODATA stands for "Iterative
  Self-Organizing Data Analysis Technique.
• It is iterative in that it repeatedly performs an
  entire classification (outputting a thematic
  raster layer) and recalculates statistics.
  "Self-Organizing" refers to the way in which it
  locates the clusters that are inherent in the
  data.
• The ISODATA clustering method uses the minimum
  spectral distance formula to form clusters. It
  begins with either arbitrary cluster means or
  means of an existing signature set, and each time
  the clustering repeats, the means of these
  clusters are shifted. The new cluster means are
  used for the next iteration.

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UNSUPERVISED CLASSIFICATION

• The ISODATA utility repeats the clustering of the
  image until either
• a maximum number of iterations has been
  performed, or
• a maximum percentage of unchanged pixels has been
  reached between two iterations.

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Identification of agriculture
Identification of forest
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Interpretation into 20 category
Aerial color image
IKONOS image
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Defining the level of Image Interpretability
Rating Scales

• National Imagery Interpretability Rating Scale
  (NIIRS) gt
• to define and measure the quality of images and
  performance of imaging systems
• NIIRS has been primarily applied in the
  evaluation of aerial imagery, it provides a
  systematic approach to measuring the quality of
  photographic or digital imagery, the performance
  of image capture devices, and the effects of
  image processing algorithms.

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Time for defining and ordering

• Aerial images in archive 10-15days

• IKONOS in archive 10-15days
• Min.order100km2

• Quick Bird in archive 10-15days
• Min.order64km2

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Time for geometric correction

• Triangulation
• Aerial (10 frames)2,5 days
• IKONOS (2 frames) 1 day
• Ortorectification the same
• Color matching and mosaic
• Aerial b/w (10 frames)3 days
• Aerial color (10 frames)4 days
• IKONOS color (2 frames) 1 day

Summary Aerial color 7,5 days Aerial b/w 6,5
days IKONOS color 3 days
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PRICE

• Aerial photos (b/w) 6,53 /km2
• Aerial photos (color) 7,84 /km2
• IKONOS images 25,80 /km2
• Quick Bird 25,80 /km2

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CONCLUSION
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