residuals.e

Residuals tests the landmarks to look for problems. It will give you a report based on landmarks, pictures and maps.

Input - files

• LMKFILES

• IMAGEFILES

• PICTLIST.TXT

• LMRKLIST.TXT

Input - stdin

• enter plim (px,km,km)

• The number of pixels an images needs to be "off" to throw the chevron (>>) flag

• The rms error of a landmark
• Sets the "bin" size for the final histogram (basically how many images are in which side categories). input operation list

     2.5, 1, 001

Output

• MAPINFO.TXT

• PICINFO.TXT

• LIMINFO.TXT

• RESIDUALS.TXT

PowerPoint Notes

Description

• Compares predicted and observed pixel, line position of the landmarks in the images
• Produces RSS of the diagonal convariance elements of each control point
  • Describes landmark position uncertainty
• Flags landmarks with residuals over specified pixel value

Control Points

• Inputs are weights for S/C uncertainties from measurement uncertainties and from a-prior shape constraints

• Control points solution results in a 3x3 output covariance for each control point. Diagonal elements are uncertainties along maplet coord. system axes, i.e. two horizontal directions & height. Typically SPC produces comparable horizontal & height uncertainties. Results are summarized as a scalar standard deviation per degree of freedom in meters

• Residuals check automatically flags errors beyond certain input values in dump file for ease of inspection

Example

Image Residuals

• The post-fit residuals between predicted and actual control point positions in all images are captured in the residuals file
• The post-fit residuals between predicted and actual control point difference between adjacent, overlapping maplets are captured in the residuals file
• Outliers in both quantities are flagged automatically based on input threshold criteria

Example

Bad & Fixed Image

Qualitative Checks

• Qualitative checks are important to verify that the quantitative error estimates are believable

• Render a single maplet, or a DEM synthesized by a collection of maplets, at the same geometry & illumination conditions as the images themselves

  • Do all the features in the images appear in the DEM ?
  • Are the smallest discernible features in the images, e.g., boulders, craters etc. visible in the DEM as well ?
  • Is the relative albedo solution such that the relative brightness of adjacent image features matches that of the DEM ?
  • Are the heights solution such that the length and overall appearance of the shadows in the DEM matches that of the image ?

How are Uncertainties Introduced in the Data

• Main types of uncertainties:
  • S/C trajectory, camera pointing, image timestamp
    • Manifest themselves mostly in the projection of image template onto the surface. Corrected by global geometry solution
  • Image noise, artifacts, smear, overall image quality
    • Manifest themselves in predicted image template brightness and in its fit to the extracted image brightness
  • Photometric model and reflectance function models

    • Show up in slopes & heights integration

  • Poor choice of a-priori parameters & data weights

    • Evident at end of each estimation step; data won’t fit well
• Often above contributions are correlated; individual contributors may not be separated until many processing steps have been taken

CategoryPrograms