Advanced Photogrammetric Mapping Ayman F. Habib

ENGO 531

Lab2: Bundle Adjustment of Scenes Captured by

Line Cameras

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Objectives

1. Analyzing photogrammetric procedures for object space reconstruction from

scenes captured by line cameras

2. Carrying out coordinate measurements in scenes captured by the IKONOS

imaging sensor

3. Approximating the exterior orientation parameters of the involved scenes

4. Approximating the ground coordinates of the selected tie points

5. Learning the format and preparing the necessary input data files for the

photogrammetric bundle adjustment

6. Analyzing and presenting the final results of the bundle adjustment procedure

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Given

• Two IKONOS scenes with more than 95% overlap

DJ_1 DJ_2

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Given

• Interior Orientation Parameters:

– Camera ID: IKONOS

– Camera type: LINE

– Focal length: 10,000.00 mm (= 10 m)

– Pixel size: 0.010 mm (= 10 μm)

– Principal point: xp = 0.0 mm, yp = 0.0 mm

– No distortions

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Given

• 20 Ground Control Points are given (accuracy ±4 cm).

• 20 Check Points are also given (approximate coordinates

for these points are provided as well).

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Given

• A general layout of the location of ground control and check points

(Filename: 40GCP_On_IKONOS.tif)

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Given • Close-up patches for location of control and check points (Filename:

GCP_descriptions.ppt)

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Given

• A general layout of the location of ground control and check points

(Filename: 40GCP_On_IKONOS_Chart.tif)

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Given

• Project file is given for this lab (Filename: Input.prj)

! line camera

! project file

! # of iteration

! max sigma

! min covariance

! min covariance2

! common alpha

! gps availability

! ins availability

! linear feature availability

! distance availability

45 1.0e-8 1.0e+3 1.0e-7 0 0 0 0 0

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Given • Camera file is given for this lab (Filename: Input.cam)

! cameraID type xp yp c

IKONOS LINE 0.0 0.0 10000.0

! dispersion of xp,yp,c

1.0e-8 0.0 0.0

0.0 1.0e-8 0.0

0.0 0.0 1.0e-8

! No fiducial marks

0

! User defined Ro

0

! Distortion Model : 1=Kraus, 2=SMAC, 3=…

1

! No distortion and array elements

6

0.0 0.0 0.0 0.0 0.0 0

1.0e-8 0.0 0.0 0.0 0.0 0.0

0.0 1.0e-8 0.0 0.0 0.0 0.0

0.0 0.0 1.0e-8 0.0 0.0 0.0

0.0 0.0 0.0 1.0e-8 0.0 0.0

0.0 0.0 0.0 0.0 1.0e-8 0.0

0.0 0.0 0.0 0.0 0.0 1.0e-8

!GPS offset: dx, dy, dz and dispersion

0.0 0.0 0.0

1E-12 0.0 0.0

0.0 1E-12 0.0

0.0 0.0 1E-12

!LINE CAMERA INFO

0 13824 0 13816 10295 10 0

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Given

• Two orientation images for each scene at the beginning and end of the

that scene (Filename: Input.ori)

! Photo_id Camera_id sigma_XY scan_time Degree_OPK Degree_XYZ

DJ_1 IKONOS 0.005 1.342 1 1

! Alpha Coeff and its Dispersion

0

! Imc Coef and its Dispersion

0

! no Ori Images

2

!Ori_no Ori_time omega phi kappa X Y Z

1 0 -5.18E+00 9.55E+00 -8.49E+01 1.57E+05 8.97E+04 9.06E+05

5 1.342 -3.85E+00 8.72E+00 -8.49E+01 1.44E+05 5.60E+04 9.08E+05

DJ_2 IKONOS 0.005 1.342 1 1

0

0

2

1 2 1.70E+01 4.06E+00 -9.31E+01 6.67E+04 -2.50E+05 8.43E+05

5 3.342 1.50E+01 4.28E+00 -9.29E+01 6.99E+04 -2.32E+05 8.49E+05

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Given

• To Sum up,

– Project file (Input.prj)

– Camera file (Input.cam)

– Orientation file (Input.ori)

– GCP file (Input.gcp)

– Image measurement file (Input.icf)

Given

Partially Given

Should be Prepared

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Given

• SW for image measurement: POOM

• SW for bundle adjustment: MSAT

POOM MSAT

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Step 1: Scene coordinate measurements

•Use the POOM program to identify and measure the given control and

check points as well as the additional tie points you choose (20 control

points + 20 check points + 15 tie points).

•Prepare ICF file

! PhotoID PointID Row Col WeightMatrix

DJ_1 K1 4123.705 1682.043 1 0 0 1

DJ_1 K3 5356.840 1922.396 1 0 0 1

DJ_1 K7 5210.758 6327.033 1 0 0 1

DJ_1 K8 4776.333 7631.419 1 0 0 1

DJ_1 K10 5586.949 9351.450 1 0 0 1

MSAT icf format for line camera

! PointID Col Row

K1 1682.043 4123.705

K3 1922.396 5356.840

K7 6327.033 5210.758

K8 7631.419 4776.333

K10 9351.450 5586.949

POOM dat format for line camera

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Step 2: Initial approximations of EOPs

•Use the given two orientation images to come up with a third one.

•It is your responsibility to devise the method for deriving EOP of the

additional orientation image.

Given EOPs Derive a third EOP

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Step 3: Initial XYZ approximations of 15 tie points

1. Use your creativity and the neighboring control/check points to

complete this task.

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Step 4: Bundle Adjustment

1. Prepare all input data files to run MSAT.

2. Implement separate bundle adjustment procedures for the following

cases:

Two orientation images, and

Three orientation images

3. Analyze the results from both cases and compare them to show the

effect of having additional orientation image.

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Step 5: Check Point Analysis

1. Use the estimated and given coordinates of the check-points to

conduct a RMSE analysis to check the quality of the reconstructed

object space.

NRMSE

N

i

1

2 value)Estimated - valueTrue(

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Deliverables and Report Preparation (1/2)

• Comments on the file format for frame and line cameras using the material in

the class notes.

• The rationale and procedure behind preparing the initial approximations of

the unknown parameters.

• The third orientation image for each scene and how it was derived.

• The final XYZ of tie and check points and the associated accuracy (found in

the results *.res and _ground2.res file). Analyze and comment on the results.

• Tabulated results for the final EOPs of the orientation images and their

variance/covariance matrix (found in the results *_eops.txt file) with

comments.

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Deliverables and Report Preparation (2/2)

• RMSE analysis of check points and comment on the final variance component.

• Inspect the final correlation image (finalCorrelation.bmp), if any parameters

are correlated; refer to the correlation.out file for details on those parameters.

Study and comment on such correlations.

• Comparison of the results from 2 and 3 orientation images.

• Explanation of any problems encountered.

• A sample (1 page max) of the measured scene coordinates (your *.icf file).

• A sample (1 page max) of the residuals (found in _resbug.$$$ output file.)

Analyze and comment on the magnitude of these residuals.

• The calculated variance component for each iteration (found in *_sigma.txt

file.)