Test F3B - Number of Images

B Test Procedures

B Test Results

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.

Add displacements for this of x2, x10, x20

P601293751H3, P601372304H3, P601371742H3, P601372862F2, P601372869F2, P601293196F2, P601292636F2

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 four images: P601293751H3, P601372862F2, P601372869F2, P601293196F2
F3B-2	Use all seven images: P601293751H3, P601372304H3, P601371742H3, P601372862F2, P601372869F2, P601293196F2, P601292636F2
F3B-3	Use fourteen images, start with the base seven images and perturb them twice.
F3B-4	Use seventy images, start with the base seven images and perturb them ten times
F3B-5	Use one hundred and forty images, start with the base seven images and perturb them twenty times

Evaluate:

Accuracy

Relative Accuracy