= Test F3B - Number of Images =

'''Goal:'''  SPC derives much of its ability to remove error by using a large number of images, and as such, error becomes Gaussian noise.  The purpose of the test is to measure the rate and magnitude of improvement with number of images.

== Test Set Up ==

'''Data:'''  
 * Approach, extra set of RC (Rotation Characterization) and closer set of RC
 * Centered around the middle of TAG Site #1, -8.030 97.218
 * Take the following images and add perturbed sets.  
 * Based upon F1b/DS results (Bennu2) with MAPLET EE0452 at 5cm resolution, -8.023 97.220.
 * Test using 4, 7, 14, 70, and 140 images
 * Uses the following images (Figure 1).  Add displacements for this of x2, x10, x20, x40  
  * P601293196, P601372856, P601372862, P601372869, P601372875      

'''Start:'''  Preliminary Survey Model, TAG Site only, based on Shape 3.7.

'''Build:'''
 * TAG site, near 0 latitude (for best evaluation).  
 * Create a 25x25m BIGMAP with MAPLETS at 5cm.  
 * Evaluate the center 20x20m to the truth model.

'''Sub-Tests'''
||F3B-1||Use only three images:  P601293196, P601372856, P601372869||
||F3B-2||Use all five images:  P601293196, P601372856, P601372862, P601372869, P601372875||
||F3B-3||Use ten images, start with the base fives images and perturb them twice.||
||F3B-4||Use fifty images, start with the base fives images and perturb them ten times||
||F3B-5||Use one hundred images, start with the base fives images and perturb them twenty times||

'''Evaluate:'''  
 * Accuracy
 * Relative Accuracy