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Results from testing the three photometric functions split into two groups characterized by differing digital terrain accuracy and model behavior. Sub-tests F3F1 and F3F2 (Lommel-Seeliger photometric function without the 2 and with the 2 respectively) performed well with minor differences in the measurements of accuracy, whereas sub-test F3F3 (Clark and Tikir photometric function) performed poorly with pervasive degradation of the digital terrain with every processing step conducted. |
TestF3F Photometric Function Sensitivity Test Results
Definitions
CompareOBJ RMS: |
The root mean square of the distance from each bigmap pixel/line location to the nearest facet of the truth OBJ. |
Key Findings
Results and Discussion
Results from testing the three photometric functions split into two groups characterized by differing digital terrain accuracy and model behavior. Sub-tests F3F1 and F3F2 (Lommel-Seeliger photometric function without the 2 and with the 2 respectively) performed well with minor differences in the measurements of accuracy, whereas sub-test F3F3 (Clark and Tikir photometric function) performed poorly with pervasive degradation of the digital terrain with every processing step conducted.
CompareOBJ RMS
Three CompareOBJ RMS values for the final 5cm resolution 20m x 20m evaluation bigmap are presented for each subtest and each S/C position and camera pointing uncertainty:
- The largest CompareOBJ RMS (approx. 65cm across subtests) is obtained by running CompareOBJ on the untranslated and unrotated evaluation model.
- The second smallest CompareOBJ RMS (approx. 15cm across subtests) is obtained by running CompareOBJ with its optimal translation and rotation option.
- The smallest CompareOBJ RMS (approx. 9cm across subtests) is obtained by manually translating the evaluation model and searching for a local CompareOBJ RMS minimum.
The CompareOBJ optimal translation routine is not optimized for the evaluation model scale (5cm pix/line resolution). Manual translations of the bigmap were therefore conducted in an attempt to find a minimum CompareOBJ RMS. The manually translated evaluation models gave the smallest CompareOBJ RMSs.
Normalized Cross Correlation Scores
The evaluation maps were compared with a truth map via a cross-correlation routine which derives a correlation score. As a guide the following scores show perfect and excellent correlations:
- A map cross-correlated with itself will give a correlation score of approx. 1.0;
- Different sized maps sampled from the same truth (for example a 1,100 x 1,100 5cm sample map and a 1,000 x 1,000 5cm sample map) give a correlation score of approx. 0.8.