cnetwinnow

After jigsaw has done a bundle adjustment on a group of overlapping Isis 3, level 1, cubes every image measure in the network will have associated residuals. A historgram of the residuals is constructed in order to determine the magnitude at which the noise (bad data) starts to dominate the solution. The points are segregated into three categories in assending order of residual magnitude: inocent, suspect, and guilty. The progam then attempts to ingore all the suspect and guilty measures based on the criteria for measure rejection provided. If guilty measures cannot be ingored a report is generated documenting why. No additional action is taken if suspect measure cannot be elliminated.

Known Issues

Not spliting a network into islands is one of the primary considerations for whether or not a measure can be ingorned. However, having even a single measure conecting mass of images A to mass of image B is currently considered sufficient for 'connecting' them together as a network. Obviously this is a necissary, though grossly insufficient requirment.

Description

This file contains a list of all cubes in the control network

Description

This file is a control network generated from programs such as autoseed or qnet. It contains the control points and associated measures.

Description

This output file contains the updated control network with the problem measures set to ignor.

Description

2 Reports will be generated: 1. FILE_PREFIXGuilty.csv, a report of apparently bad points that could not be set to ignored, 2. FILE_PREFIXIgnored.csv, a report of all the measures that were set to ignor.

Description

The program will build and analyze a histogram of the R^2 residual lengths. This prameter tells the software how wide to make each bin. Unless the user has studied the behaviour of residuals (particularly with regard to their networks), we do not advise changing the default.

Description

If all of the measurements in network belong to Gaussian distribution (one of the typical assumptions in least squares analysis) then the residual histogram should be strictly decreasing in the tail. There should be only one local maxima near the median (a results of using equal distance bins instead of equal area). All other local maxima are 'bumps' and reveal where the assumption of Gaussian noise is breaking down. The SUSPECT_BUMP_PERCENT is the minimum bump height (in percentage of the preceding local minima) for a bump to be considered suspect. And barring user override will be the boundary between inocent and suspect points. Changing the default is not typically recomended.

Description

If all of the measurements in network belong to Gaussian distribution (one of the typical assumptions in least squares analysis) then the residual histogram should be strictly decreasing in the tail. There should be only one local maxima near the median (a results of using equal distance bins instead of equal area). All other local maxima are 'bumps' and reveal where the assumption of Gaussian noise is breaking down. The GUILTY_BUMP_PERCENT is the minimum bump height (in percentage of the preceding local minima) for a bump to be considered guilty. And barring user override will be the boundary between suspect and guilty points. Changing the default is not typically recomended.

Description

SUSPECT_FLOOR is the lowest that the boundary between inocent and suspect residual magnitudes will slip. Histogram analysis may however result in a higher magnitude boundary.

Description

GUILTY_FLOOR is the highest that the boundary between suspect and guilty residual magnitudes will slip. Histogram analysis may however result in a lower magnitude boundary--strickly greater than the inocent-suspect boundary.

Description

Maximum precent reduction to the area of the convex hull of the measures in an image.

Description

Maximum precent reduction of the measures in an image.