Test 05: OptimalF2
Aim and Objectives
Purpose of test: To obtain highest possible topographic accuracy and resolution for TAG site 1, given perfect camera position and pointing and optimal SPC image suite.
Additional Objectives
 To obtain userintheloop and serverintheloop RMS error variations.
 To obtain userintheloop procedural variation.
 To obtain beginner/expert user variation.
Acronyms
DTM 
Digital Terrain Model 
GSD 
Ground Sample Distance 
RMS 
Root Mean Square 
SPC 
Stereophotoclinometry 
TAG 
Touch And Go 
Definitions and Disambiguation
Formal Uncertainty 
The RMS landmark position uncertainty of the bigmap as output by the utility program RESIDUALS. 
CompareOBJ RMS 
RMS persample distance to nearest truth surface point as output by the utility program CompareOBJ. 
Methodology
Data
 Data was generated by Imager_mg using shape3.8 on ormacsrv1.
 PT10A  Centered at the middle of TAG site one.
 Image Suite: Optimal flight path, image resolutions: 50cm, 20cm, 10cm, 5cm.
 S/C:
 Every 20° azimuth angle, 45° zenith angle
 Sun:
 0, 90°, 180°, 270° azimuth angle, 45 zenith angle
 135° azimuth angle, 30° zenith angle
 S/C:
Spacecraft:
Sun:
User/Serverintheloop:
User 
Experience 
Server 
Number of Core Processors 
TCampbell 
Intermediate 
?Ormacsrv2 
?8 
KDrozd 
Intermediate 
Ormacsrv1 
8 
DLambert 
Beginner 
Ormacsrv3 
6 
EPalmer 
Experienced 
? 
? 
JWeirich 
Intermediate 
DD 
12 
Bigmaps
The following TAG1 bigmaps were tiled/iterated and evaluated:
Starting topography defined from: START1.MAP:
 GSD = 25cm;
 Q size = 150;
 width = 75m;
 center lat/wlong = 8.027, 262.768.
DTM Bigmap parameters:
Step 
GSD(cm) 
Maplet Overlap Factor 
Approx. Maplet Overlap 
Q Size 
Width 
35cmTiling 
35 
1/1.3 
60% 
112 
60m 
18cmTiling 
18 
1/1.3 
60% 
217 
61m 
9cmTiling 
9 
1/1.3 
60% 
434 
61m 
5cmTiling 
5 
1/1.3 
60% 
780 
62m 
Evaluation Bigmap parameters:
 GSD = 5cm;
 Q size = 500;
 width = 50m;
Tiling Parameters
Tiling parameters may have varied between users:
Parameter 
TCampbell 
KDrozd 
DLambert 
EPalmer 
JWeirich 
Image elimination: INVLIM 
0 
0 
0 
? 
0 
Image elimination: SLIM 
60 (@35cm), 50 (o.w.) 
50 
60 (@35cm), 50 (o.w.) 
? 
50 
Image elimination: CLIM 
.5 
.5 
.5 
? 
.5 
Image elimination: ILIM 
.5 
.5 
.5 
? 
.5 
Image elimination: RSMIN 
0 (@35cm), .25 (o.w.) 
.25 
0 (@35cm), .25 (o.w.) 
? 
.25 
Image elimination: RSMAX 
3 
3 
3 
? 
3 
Calculate Central Vector (v, 1) 
YES 
YES 
YES 
? 
YES 
Differential Stereo (2, 6) 
YES 
YES 
YES 
? 
YES 
Shadows (2, 7) 
NO 
NO 
NO 
? 
NO 
alphanumerics in brackets refer to Lithos/LithosP menu options.
Iteration Parameters
Iteration parameters varied between users:
Parameter 
TCampbell 
KDrozd 
DLambert 
EPalmer 
JWeirich 
Reset albedo/slopes (a, y, y) 
NO 
YES 
YES 
? 
YES 
Calculate Central Vector (v, 1) 
YES 
YES 
YES 
? 
YES 
Differential Stereo (2, 6) 
NO 
NO 
NO 
? 
NO 
Shadows (2, 7) 
NO 
YES 
NO 
? 
YES 
alphanumerics in brackets refer to Lithos/LithosP menu options.
Results
Please see Tables and Figures.
Discussion
User Differences in Tiling and Iteration Parameters
The only differences in tiling parameters pertain to the autoelimination of images during the initial 35cm tiling. The maximum permissible emission angle varied between 50 and 60 degrees. The minimum allowable ration between image scale and misplace varied between 0 and 0.25. All other tiling step parameters were the same between users.
Iteration parameters however varied greatly between users, with no two users applying an identical set of parameters. All users calculated the central vector (v1), however not all users reset albedo/slopes, conditioned with differential stereo and/or conditioned with shadows.
User Differences in Processing Steps
Since users had different criteria for triggering the cessation of iteration and continuing to the next tiling step (such as 2 iterations per tiling step, or, no further change in evaluation statistics), users iterated solutions a varied number of times.
Number of iterations conducted at each GSD step:
Processing Step 
TCampbell 
KDrozd 
DLambert 
EPalmer 
JWeirich 
35cm Iterations 
2 
6 
5 
6 
2 
18cm Iterations 
2 
2 
6 
1 
2 
9cm Iterations 
2 
1 
2 
 
2 
5cm Iterations 
2 
 
 
 
1 
User Errors
Additionally, two users applied incorrect or nonstandard processing procedures for some portion of the test related to their level of experience and training:
 DLambert calculated statistics on an incorrectly sized evaluation bigmap during the 35cm tiling and first iteration leading to loss of statistics data for these processing steps;
 DLambert failed to eliminate low correlating images during the first three 35cm iteration steps, leading to initial problems in the topography which are reflected in the formal uncertainties and RMS errors. The errors receded at the 18cm tiling step.
 KDrozd used different parameters in the utility program FITS2OBJ, upstream of obtaining the CompareOBJ RMS error, as other team members, leading to differing RMS error statistics which do not necessarily reflect differences in the evaluation bigmap compared to other users' evaluation bigmaps.
User Differences in Evaluation Statistics
CompareOBJ RMS errors show large variation from user to user. Some of these variations can be directly linked to user procedural differences, for example the high RMS errors through the 35cm iterations for DLambert and KDrozd are directly related to incorrect and nonstandard processing (see above section). Other differences reflect some combination of:
 variation in iteration parameters (see above section);
 variation in number of iterations conducted;
 variation in user skill and experience in manually registering/eliminating problem images;
 serverbased variation;
 numberofcoreprocessorsbased variation:
 SPC uncertainty due to random seeds utilized in maplet slope and height calculations.
Formal uncertainties were more similar between users, differences can be directly linked to user procedural differences, specifically incorrect and nonstandard processing covered in the previous section.
Overall SPC Performance
All users obtain formal uncertainties below the highest image resolution (< 5cm).
Final CompareOBJ RMS errors vary. The two final solutions which reached a GSD of 5cm obtained an RMS error of 12.3cm and 4.5cm. Slight improvements in CompareOBJ RMS can be gained by using the CompareOBJ optimal translation/rotation utilities.
Conclusions and Recommendations
Formal uncertainties performed well in respect to both betweenuser variation and final performance measures. However, the CompareOBJ RMS error did not, showing high variability usertouser and high variability in final performance measures.
Usertouser differences can be minimized in future testing through a combination of standardization of tiling/iteration parameters and iteration cessation criteria, and increase in user skill and experience.
Tiling and iteration parameters should be investigated to find the bestpractice for standardized seed file sets. This recommendation is addressed in Test06  FindHeights.
To further investigate the performance of SPC with perfect camera position and pointing and optimal viewing conditions, a suite of higherresolution images will be added and the best performing 5cm DTM will be tiled and iterated at higher ground sample distances. This recommendation led to the implementation of Test08  OptimalUltraF2.
Tables and Figures
RMS Distance (cm)  Compare OBJ (with no translation/rotation)
Resolution (cm) 
Step 
Diane 
Eric 
John 
Kris 
Tanner 
35 
35cmTiling 

7.06 

7.06 
8.35 
35 
35cmIteration 1 

6.95 

7.15 
8.49 
35 
35cmIteration 2 
11.51 
7.15 
7.13 
7.44 
8.58 
35 
35cmIteration 3 
11.76 
7.6 

7.44 

35 
35cmIteration 4 
11.85 
8.04 

9.10 

35 
35cmIteration 5 
11.31 
7.57 

11.05 

35 
35cmIteration 6 

8.81 

13.39 

18 
18cmTiling 
8.78 

5.43 
8.79 
6.49 
18 
18cmIteration 1 
8.62 
8.12 
6.29 
9.37 
6.50 
18 
18cmIteration 2 
8.57 

7.45 
10.60 
6.58 
18 
18cmIteration 3 
8.57 




18 
18cmIteration 4 
8.56 




18 
18cmIteration 5 
8.55 




18 
18cmIteration 6 
8.54 




9 
9cmTiling 
5.47 

6.00 
8.49 
4.98 
9 
9cmIteration 1 
5.28 

7.39 
9.80 
4.92 
9 
9cmIteration 2 
5.27 

9.15 

4.92 
5 
5cmTiling 


7.68 

4.47 
5 
5cmIteration 1 


12.31 

4.44 
5 
5cmIteration 2 




4.45 
RMS Distance (cm)  Compare OBJ (with optimal translation/rotation)
Resolution (cm) 
Step 
Diane 
Kris 



RMS without Translation 
RMS with Optimal 
RMS with Optimal 
RMS with Manual 
RMS without Translation 
RMS with Optimal 
35 
35cmTiling 




7.06 

35 
35cmIteration 1 




7.15 

35 
35cmIteration 2 
11.51 

7.44 



35 
35cmIteration 3 
11.76 
11.40 
10.89 

7.44 

35 
35cmIteration 4 
11.85 
11.40 
10.92 

9.10 

35 
35cmIteration 5 
11.31 
10.98 
10.35 

11.05 

35 
35cmIteration 6 




13.39 

18 
18cmTiling 
8.62 
8.78 
8.15 

8.79 

18 
18cmIteration 1 
8.62 
8.44 
8.19 

9.37 

18 
18cmIteration 2 
8.57 
8.38 
8.09 

10.60 

18 
18cmIteration 3 
8.57 
8.44 
8.06 



18 
18cmIteration 4 
8.56 
8.40 
8.02 



18 
18cmIteration 5 
8.55 
8.41 
7.96 



18 
18cmIteration 6 
8.54 
8.44 
7.96 



9 
9cmTiling 
5.47 
6.06 
5.49 
6.57 
8.49 

9 
9cmIteration 1 
5.28 
5.40 
5.26 

9.80 

9 
9cmIteration 2 
5.27 
5.45 
5.32 


6.13 
5 
5cmTiling 






5 
5cmIteration 1 






5 
5cmIteration 2 






Formal Uncertainty (cm)  RESIDUALS
Resolution (cm) 
Step 
Diane 
Eric 
John 
Kris 
Tanner 
35 
35cmTiling 

5.48 

5.49 

35 
35cmIteration 1 

6.32 

6.91 

35 
35cmIteration 2 
29.78 
4.76 

5.36 
5.72 
35 
35cmIteration 3 
39.12 
4.71 

5.45 

35 
35cmIteration 4 
14.74 
4.87 

5.98 

35 
35cmIteration 5 
12.69 
5.04 

6.72 

35 
35cmIteration 6 

5.23 

7.51 

18 
18cmTiling 
8.78 
9.17 
3.12 
5.11 
4.34 
18 
18cmIteration 1 
7.90 

2.97 
3.98 
4.45 
18 
18cmIteration 2 
7.23 

3.33 
3.98 
4.22 
18 
18cmIteration 3 
6.84 




18 
18cmIteration 4 
6.61 




18 
18cmIteration 5 
6.47 




18 
18cmIteration 6 
6.40 




9 
9cmTiling 
3.82 

2.24 
2.86 
2.54 
9 
9cmIteration 1 
3.53 

2.57 
2.99 
2.46 
9 
9cmIteration 2 
3.18 

3.01 

2.26 
5 
5cmTiling 


2.99 

1.59 
5 
5cmIteration 1 


3.97 

1.43 
5 
5cmIteration 2 




1.35 
Normalized Cross Correlation Score
Resolution (cm) 
Step 
Diane 
Eric 
John 
Kris 
Tanner 
35 
35cmTiling 





35 
35cmIteration 1 





35 
35cmIteration 2 
0.6433 




35 
35cmIteration 3 
0.6424 




35 
35cmIteration 4 
0.6405 




35 
35cmIteration 5 
0.6423 




35 
35cmIteration 6 





18 
18cmTiling 
0.7360 




18 
18cmIteration 1 
0.7373 




18 
18cmIteration 2 
0.7377 




18 
18cmIteration 3 
0.7378 




18 
18cmIteration 4 
0.7380 




18 
18cmIteration 5 
0.7381 




18 
18cmIteration 6 
0.7381 




9 
9cmTiling 
0.8118 




9 
9cmIteration 1 
0.8113 




9 
9cmIteration 2 
0.8113 




5 
5cmTiling 





5 
5cmIteration 1 





5 
5cmIteration 2 





35cm GSD Maplets  50cm Resolution Images
18cm GSD Maplets  20cm Resolution Images
9cm GSD Maplets  10cm Resolution Images