= 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. A detailed analysis of the behavior of F3F3 is reported here: [[Test F3F3 - Analysis]].

=== 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.